PiPred - a deep-learning method for prediction of π-helices in protein sequences

AlphaFold2 captures the conformational landscape of the HAMP signaling domain

In this study, we present a conformational landscape of 5000 AlphaFold2 models of the Histidine kinases, Adenyl cyclases, Methyl-accepting proteins and Phosphatases (HAMP) domain, a short helical bundle that transduces signals from sensors to effectors in two-component signaling proteins such as sensory histidine kinases and chemoreceptors. The landscape reveals the conformational variability of the HAMP domain, including rotations, shifts, displacements, and tilts of helices, many combinations of which have not been observed in experimental structures. HAMP domains belonging to a single family tend to occupy a defined region of the landscape, even when their sequence similarity is low, suggesting that individual HAMP families have evolved to operate in a specific conformational range. The functional importance of this structural conservation is illustrated by poly-HAMP arrays, in which HAMP domains from families with opposite conformational preferences alternate, consistent with the rotational model of signal transduction. The only poly-HAMP arrays that violate this rule are predicted to be of recent evolutionary origin and structurally unstable. Finally, we identify a family of HAMP domains that are likely to be dynamic due to the presence of a conserved pi-helical bulge. All code associated with this work, including a tool for rapid sequence-based prediction of the rotational state in HAMP domains, is deposited at https://github.com/labstructbioinf/HAMPpred.

Non-canonical helical transitions and conformational switching are associated with characteristic flexibility and disorder indices in TRP and Kv channels

Structural evidence and much experimental data have demonstrated the presence of non-canonical helical substructures (π and 3₁₀) in regions of great functional relevance both in TRP as in Kv channels. Through an exhaustive compositional analysis of the sequences underlying these substructures, we find that each of them is associated with characteristic local flexibility profiles, which in turn are implicated in significant conformational rearrangements and interactions with specific ligands. We found that α-to-π helical transitions are associated with patterns of local rigidity whereas α-to-3₁₀ transitions are mainly leagued with high local flexibility profiles. We also study the relationship between flexibility and protein disorder in the transmembrane domain of these proteins. By contrasting these two parameters, we located regions showing a sort of structural discrepancy between these similar but not identical protein attributes. Notably, these regions are presumably implicated in important conformational rearrangements during the gating in those channels. In that sense, finding these regions where flexibility and disorder are not proportional allows us to detect regions with potential functional dynamism. From this point of view, we highlighted some conformational rearrangements that occur during ligand binding events, the compaction, and refolding of the outer pore loops in several TRP channels, as well as the well-known S4 motion in Kv channels.

Structural basis of mammalian respiratory complex I inhibition by medicinal biguanides

The molecular mode of action of biguanides, including the drug metformin, which is widely used in the treatment of diabetes, is incompletely characterized. Here, we define the inhibitory drug-target interaction(s) of a model biguanide with mammalian respiratory complex I by combining cryo-electron microscopy and enzyme kinetics. We interpret these data to explain the selectivity of biguanide binding to different enzyme states. The primary inhibitory site is in an amphipathic region of the quinone-binding channel, and an additional binding site is in a pocket on the intermembrane-space side of the enzyme. An independent local chaotropic interaction, not previously described for any drug, displaces a portion of a key helix in the membrane domain. Our data provide a structural basis for biguanide action and enable the rational design of medicinal biguanides.

What defines biomimetic and bioinspired science and engineering?

Biomimicry, biomimesis and bioinspiration define distinctly different approaches for deepening the understanding of how living systems work and employing this knowledge to meet pressing demands in engineering. Biomimicry involves shear imitation of biological structures that most often do not reproduce the functionality that they have while in the living organisms. Biomimesis aims at reproduction of biological structure-function relationships and advances our knowledge of how different components of complex living systems work. Bioinspiration employs this knowledge in abiotic manners that are optimal for targeted applications. This article introduces and reviews these concepts in a global historic perspective. Representative examples from charge-transfer science and solar-energy engineering illustrate the evolution from biomimetic to bioinspired approaches and show their importance. Bioinspired molecular electrets, aiming at exploration of dipole effects on charge transfer, demonstrate the pintail impacts of biological inspiration that reach beyond its high utilitarian values. The abiotic character of bioinspiration opens doors for the emergence of unprecedented properties and phenomena, beyond what nature can offer.

A structural signature motif enlightens the origin and diversification of nuclear receptors

Nuclear receptors are ligand-activated transcription factors that modulate gene regulatory networks from embryonic development to adult physiology and thus represent major targets for clinical interventions in many diseases. Most nuclear receptors function either as homodimers or as heterodimers. The dimerization is crucial for gene regulation by nuclear receptors, by extending the repertoire of binding sites in the promoters or the enhancers of target genes via combinatorial interactions. Here, we focused our attention on an unusual structural variation of the α-helix, called π-turn that is present in helix H7 of the ligand-binding domain of RXR and HNF4. By tracing back the complex evolutionary history of the π-turn, we demonstrate that it was present ancestrally and then independently lost in several nuclear receptor lineages. Importantly, the evolutionary history of the π-turn motif is parallel to the evolutionary diversification of the nuclear receptor dimerization ability from ancestral homodimers to derived heterodimers. We then carried out structural and biophysical analyses, in particular through point mutation studies of key RXR signature residues and showed that this motif plays a critical role in the network of interactions stabilizing homodimers. We further showed that the π-turn was instrumental in allowing a flexible heterodimeric interface of RXR in order to accommodate multiple interfaces with numerous partners and critical for the emergence of high affinity receptors. Altogether, our work allows to identify a functional role for the π-turn in oligomerization of nuclear receptors and reveals how this motif is linked to the emergence of a critical biological function. We conclude that the π-turn can be viewed as a structural exaptation that has contributed to enlarging the functional repertoire of nuclear receptors.

The origin of novelties is a central topic in evolutionary biology. A fundamental question is how organisms constrained by natural selection can divert from existing schemes to set up novel structures or pathways. Among the most important strategies are exaptations, which represent pre-adaptation strategies. Many examples exist in biology, at both morphological and molecular levels, such as the one reported here that focuses on an unusual structural feature called the π-turn. It is found in the structure of the most ancestral nuclear receptors RXR and HNF4. The analyses trace back the complex evolutionary history of the π-turn to more than 500 million years ago, before the Cambrian explosion and show that this feature was essential for the heterodimerization capacity of RXR. Nuclear receptor lineages that emerged later in evolution lost the π-turn. We demonstrate here that this loss in nuclear receptors that heterodimerize with RXR was critical for the emergence of high affinity receptors, such as the vitamin D and the thyroid hormone receptors. On the other hand, the conserved π-turn in RXR allowed it to accommodate multiple heterodimer interfaces with numerous partners. This structural exaptation allowed for the remarkable diversification of nuclear receptors.

SAINT: self-attention augmented inception-inside-inception network improves protein secondary structure prediction

MOTIVATION

Protein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g. X-ray crystallography and nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the SS of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8-class (Q8) resolution of SS contains more useful information and is much more challenging than the Q3 prediction.

RESULTS

We present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception network in order to effectively capture both the short- and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step toward the accurate and reliable prediction of SSs of proteins.

AVAILABILITY AND IMPLEMENTATION

SAINT is freely available as an open-source project at https://github.com/SAINTProtein/SAINT.

Deep learning for mining protein data

The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives: residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.