Evolutionary couplings detect side-chain interactions

Patterns of amino acid covariation in large protein sequence alignments can inform the prediction of de novo protein structures, binding interfaces, and mutational effects. While algorithms that detect these so-called evolutionary couplings between residues have proven useful for practical applications, less is known about how and why these methods perform so well, and what insights into biological processes can be gained from their application. Evolutionary coupling algorithms are commonly benchmarked by comparison to true structural contacts derived from solved protein structures. However, the methods used to determine true structural contacts are not standardized and different definitions of structural contacts may have important consequences for interpreting the results from evolutionary coupling analyses and understanding their overall utility. Here, we show that evolutionary coupling analyses are significantly more likely to identify structural contacts between side-chain atoms than between backbone atoms. We use both simulations and empirical analyses to highlight that purely backbone-based definitions of true residue–residue contacts (i.e., based on the distance between Cα atoms) may underestimate the accuracy of evolutionary coupling algorithms by as much as 40% and that a commonly used reference point (Cβ atoms) underestimates the accuracy by 10–15%. These findings show that co-evolutionary outcomes differ according to which atoms participate in residue–residue interactions and suggest that accounting for different interaction types may lead to further improvements to contact-prediction methods.

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De novo design of a reversible phosphorylation-dependent switch for membrane targeting

Nature Communications ◽

10.1038/s41467-021-21622-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Leon Harrington ◽

Jordan M. Fletcher ◽

Tamara Heermann ◽

Derek N. Woolfson ◽

Petra Schwille

Keyword(s):

Protein Interactions ◽

Lipid Membrane ◽

De Novo ◽

Protein Localization ◽

Protein Structures ◽

Spatiotemporal Pattern ◽

Membrane Targeting ◽

Protein Protein Interactions ◽

Reversible Phosphorylation ◽

Potential Applications

AbstractModules that switch protein-protein interactions on and off are essential to develop synthetic biology; for example, to construct orthogonal signaling pathways, to control artificial protein structures dynamically, and for protein localization in cells or protocells. In nature, the E. coli MinCDE system couples nucleotide-dependent switching of MinD dimerization to membrane targeting to trigger spatiotemporal pattern formation. Here we present a de novo peptide-based molecular switch that toggles reversibly between monomer and dimer in response to phosphorylation and dephosphorylation. In combination with other modules, we construct fusion proteins that couple switching to lipid-membrane targeting by: (i) tethering a ‘cargo’ molecule reversibly to a permanent membrane ‘anchor’; and (ii) creating a ‘membrane-avidity switch’ that mimics the MinD system but operates by reversible phosphorylation. These minimal, de novo molecular switches have potential applications for introducing dynamic processes into designed and engineered proteins to augment functions in living cells and add functionality to protocells.

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Protein Interaction Z Score Assessment (PIZSA): an empirical scoring scheme for evaluation of protein–protein interactions

Nucleic Acids Research ◽

10.1093/nar/gkz368 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W331-W337 ◽

Cited By ~ 3

Author(s):

Ankit A Roy ◽

Abhilesh S Dhawanjewar ◽

Parichit Sharma ◽

Gulzar Singh ◽

M S Madhusudhan

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Three Dimensional ◽

Optimal Number ◽

Dimensional Structure ◽

Side Chain ◽

Interface Residue ◽

Z Score ◽

Protein Protein Interactions ◽

Residue Contacts

Abstract Our web server, PIZSA (http://cospi.iiserpune.ac.in/pizsa), assesses the likelihood of protein–protein interactions by assigning a Z Score computed from interface residue contacts. Our score takes into account the optimal number of atoms that mediate the interaction between pairs of residues and whether these contacts emanate from the main chain or side chain. We tested the score on 174 native interactions for which 100 decoys each were constructed using ZDOCK. The native structure scored better than any of the decoys in 146 cases and was able to rank within the 95th percentile in 162 cases. This easily outperforms a competing method, CIPS. We also benchmarked our scoring scheme on 15 targets from the CAPRI dataset and found that our method had results comparable to that of CIPS. Further, our method is able to analyse higher order protein complexes without the need to explicitly identify chains as receptors or ligands. The PIZSA server is easy to use and could be used to score any input three-dimensional structure and provide a residue pair-wise break up of the results. Attractively, our server offers a platform for users to upload their own potentials and could serve as an ideal testing ground for this class of scoring schemes.

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Socket2: A Program for Locating, Visualising, and Analysing Coiled-coil Interfaces in Protein Structures

Bioinformatics ◽

10.1093/bioinformatics/btab631 ◽

2021 ◽

Author(s):

Prasun Kumar ◽

Derek N Woolfson

Keyword(s):

Protein Interactions ◽

De Novo ◽

Protein Structures ◽

Coiled Coil ◽

Biological Research ◽

Biological Processes ◽

Protein Protein Interactions ◽

Wide Range ◽

Core Packing ◽

Natural Amino Acids

Abstract Motivation Protein-protein interactions are central to all biological processes. One frequently observed mode of such interactions is the α-helical coiled coil (CC). Thus, an ability to extract, visualise, and analyse CC interfaces quickly and without expert guidance would facilitate a wide range of biological research. In 2001, we reported Socket, which locates and characterises CCs in protein structures based on the knobs-into-holes (KIH) packing between helices in CCs. Since then, studies of natural and de novo designed CCs have boomed, and the number of CCs in the RCSB PDB has increased rapidly. Therefore, we have updated Socket and made it accessible to expert and non-expert users alike. Results The original Socket only classified CCs with up to 6 helices. Here, we report Socket2, which rectifies this oversight to identify CCs with any number of helices, and KIH interfaces with any of the 20 proteinogenic residues or incorporating non-natural amino acids. In addition, we have developed a new and easy-to-use web server with additional features. These include the use of NGL Viewer for instantly visualising CCs, and tabs for viewing the sequence repeats, helix-packing angles, and core-packing geometries of CCs identified and calculated by Socket2. Availability and implementation Socket2 has been tested on all modern browsers. It can be accessed freely at http://coiledcoils.chm.bris.ac.uk/socket2/home.html. The source code is distributed using an MIT license and available to download under the Downloads tab of the Socket2 home page.

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Opportunities and limitations in applying coevolution-derived contacts to protein structure prediction

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2014-0013 ◽

2014 ◽

Vol 10 (4) ◽

Cited By ~ 4

Author(s):

Stuart Tetchner ◽

Tomasz Kosciolek ◽

David T. Jones

Keyword(s):

Protein Interactions ◽

Structure Prediction ◽

Tertiary Structure ◽

Protein Structures ◽

Initial Assessment ◽

Protein Protein Interactions ◽

Sequence Alignments ◽

Data Dependencies ◽

Long Time ◽

Alternative Approaches

AbstractThe prospect of identifying contacts in protein structures purely from aligned protein sequences has lured researchers for a long time, but progress has been modest until recently. Here, we reviewed the most successful methods for identifying structural contacts from sequence and how these methods differ and made an initial assessment of the overlap of predicted contacts by alternative approaches. We then discussed the limitations of these methods and possibilities for future development and highlighted the recent applications of contacts in tertiary structure prediction, identifying the residues at the interfaces of protein-protein interactions, and the use of these methods in disentangling alternative conformational states. Finally, we identified the current challenges in the field of contact prediction, concentrating on the limitations imposed by available data, dependencies on the sequence alignments, and possible future developments.

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A Reproducibility Analysis-based Statistical Framework for Residue-Residue Evolutionary Coupling Detection

10.1101/2021.02.01.429092 ◽

2021 ◽

Author(s):

Yunda Si ◽

Chengfei Yan

Keyword(s):

Protein Interactions ◽

Rna Structure ◽

Protein Protein Interactions ◽

Multiple Sequence ◽

Contact Prediction ◽

Statistical Framework ◽

Residue Contacts ◽

Direct Coupling Analysis ◽

General Statistical ◽

Evolutionary Coupling

AbstractDirect coupling analysis (DCA) has been widely used to predict residue-residue contacts to assist protein/RNA structure and interaction prediction. However, effectively selecting residue pairs for contact prediction according to the result of DCA is a non-trivial task, since the number of highly predictive residue pairs and the coupling scores obtained from DCA are highly dependent on the number and the length of the homologous sequences forming the multiple sequence alignment, the detailed settings of the DCA algorithm, the functional characteristics of the macromolecule, etc. In this study, we present a general statistical framework for selecting predictive residue pairs through significant evolutionary coupling detection, referred to as IDR-DCA, which is based on reproducibility analysis of the coupling scores from replicated DCA. IDR-DCA was applied to select residue pairs for contact prediction for 150 proteins, 30 protein-protein interactions and 36 RNAs, in which we applied three widely used DCA software to perform the DCA. We show that with the application of IDR-DCA, the predictive residue pairs can be effectively selected through a universal threshold independent on the DCA software.

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Accurate prediction of residue-residue contacts across homo-oligomeric protein interfaces through deep leaning

10.1101/2020.09.13.295196 ◽

2020 ◽

Author(s):

Yumeng Yan ◽

Sheng-You Huang

Keyword(s):

Deep Learning ◽

Protein Interactions ◽

Structure Prediction ◽

High Accuracy ◽

Protein Protein Interactions ◽

Sequence Alignments ◽

Contact Prediction ◽

Protein Interfaces ◽

Residue Contacts ◽

Oligomeric Protein

AbstractProtein-protein interactions play a fundamental role in all cellular processes. Therefore, determining the structure of protein-protein complexes is crucial to understand their molecular mechanisms and develop drugs targeting the protein-protein interactions. Recently, deep learning has led to a breakthrough in intraprotein contact prediction, achieving an unusual high accuracy in recent CASP structure prediction challenges. However, due to the limited number of known homologous protein-protein interactions and the challenge to generate joint multiple sequence alignments (MSA) of two interacting proteins, the advances in inter-protein contact prediction remain limited. Here, we have proposed a deep learning model to predict inter-protein residue-residue contacts across homo-oligomeric protein interfaces, named as DeepHomo, by integrating evolutionary coupling, sequence conservation, distance map, docking pattern, and physic-chemical information of monomers. DeepHomo was extensively tested on both experimentally determined structures and realistic CASP-CAPRI targets. It was shown that DeepHomo achieved a high accuracy of >60% for the top predicted contact and outperformed state-of-the-art direct-coupling analysis (DCA) and machine learning (ML)-based approaches. Integrating predicted contacts into protein docking with blindly predicted monomer structures also significantly improved the docking accuracy. The present study demonstrated the success of DeepHomo in inter-protein contact prediction. It is anticipated that DeepHomo will have a far-reaching implication in the inter-protein contact and structure prediction for protein-protein interactions.

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Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

10.26434/chemrxiv.11663337.v3 ◽

2020 ◽

Author(s):

Salvador Guardiola ◽

Monica Varese ◽

Xavier Roig ◽

Jesús Garcia ◽

Ernest Giralt

Keyword(s):

Protein Interactions ◽

Cyclic Peptides ◽

General Framework ◽

Large Scale ◽

De Novo ◽

Inhibitory Effect ◽

Original Text ◽

Protein Protein Interactions ◽

Retraction Notice ◽

Pharmaceutical Properties

NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above. ------------------------------------------------------------------------ Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-i3 and PD-i6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our de novo design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.

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Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

10.26434/chemrxiv.11663337 ◽

2020 ◽

Author(s):

Salvador Guardiola ◽

Monica Varese ◽

Xavier Roig ◽

Jesús Garcia ◽

Ernest Giralt

Keyword(s):

Protein Interactions ◽

Cyclic Peptides ◽

General Framework ◽

Large Scale ◽

De Novo ◽

Inhibitory Effect ◽

Original Text ◽

Protein Protein Interactions ◽

Retraction Notice ◽

Pharmaceutical Properties

NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above. ------------------------------------------------------------------------ Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-i3 and PD-i6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our de novo design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.

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Short Linear Motifs Characterizing Snake Venom and Mammalian Phospholipases A2

Toxins ◽

10.3390/toxins13040290 ◽

2021 ◽

Vol 13 (4) ◽

pp. 290

Author(s):

Caterina Peggion ◽

Fiorella Tonello

Keyword(s):

Snake Venom ◽

Protein Interactions ◽

Biological Properties ◽

Protein Protein Interactions ◽

Sequence Alignments ◽

Toxic Activity ◽

Short Linear Motifs ◽

Phospholipases A2 ◽

Group I ◽

Linear Motifs

Snake venom phospholipases A2 (PLA2s) have sequences and structures very similar to those of mammalian group I and II secretory PLA2s, but they possess many toxic properties, ranging from the inhibition of coagulation to the blockage of nerve transmission, and the induction of muscle necrosis. The biological properties of these proteins are not only due to their enzymatic activity, but also to protein–protein interactions which are still unidentified. Here, we compare sequence alignments of snake venom and mammalian PLA2s, grouped according to their structure and biological activity, looking for differences that can justify their different behavior. This bioinformatics analysis has evidenced three distinct regions, two central and one C-terminal, having amino acid compositions that distinguish the different categories of PLA2s. In these regions, we identified short linear motifs (SLiMs), peptide modules involved in protein–protein interactions, conserved in mammalian and not in snake venom PLA2s, or vice versa. The different content in the SLiMs of snake venom with respect to mammalian PLA2s may result in the formation of protein membrane complexes having a toxic activity, or in the formation of complexes whose activity cannot be blocked due to the lack of switches in the toxic PLA2s, as the motif recognized by the prolyl isomerase Pin1.

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