Improving the Quality of Co-evolution Intermolecular Contact Prediction with DisVis

The steep rise in available protein sequences and structures has paved the way for bioinformatics approaches to predict residue-residue interactions in protein complexes. Multiple sequence alignments are commonly used in intermolecular contact predictions to identify co-evolving residues. These contacts, however, often include false positives (FPs), which may impair their use to predict three dimensional structures of biomolecular complexes and affect the accuracy of the generated models. Previously, we have developed DisVis to identify false positive data in mass spectrometry cross-linking data. DisVis allows to assess the accessible interaction space between two proteins consistent with a set of distance restraints. Here, we investigate if a similar approach could be applied to co-evolution predicted contacts in order to improve their precision prior to using them for modelling complexes. In this work we analyze co-evolution contact predictions with DisVis in order to identify putative FPs for a set of 26 protein-protein complexes. Next, the DisVis-reranked and the original co-evolution contacts are used to model the complexes with our integrative docking software HADDOCK using different filtering scenarios. Our results show that HADDOCK is robust with respect to the precision of the predicted contacts due to the 50% random contact removal during docking and using DisVis filtering for low precision contact data. DisVis can thus have a beneficial effect on low quality data, but overall HADDOCK can accommodate FP restraints without negatively impacting the quality of the resulting models. Other more precision-sensitive docking protocols might, however, benefit from the increased precision of the predicted contacts after DisVis filtering

Novel insights into guide RNA 5ʹ-Nucleoside/Tide binding by human argonaute 2

The human Argonaute 2 (hAgo2) protein is a key player of RNA interference (RNAi). Upon complex formation with small non-coding RNAs, the protein initially interacts with the 51-end of a given guide RNA through multiple interactions within the MID domain. This interaction has been reported to show a strong bias for U and A over C and G at the 5ʹ-position. Performing molecular dynamics simulations of binary hAgo2/OH–guide–RNA complexes, we show that hAgo2 is a highly flexible protein capable of binding to guide strands with all four possible 51-bases. Especially, in the case of C and G this is associated with rather large individual conformational rearrangements affecting the MID, PAZ and even the N-terminal domains to different degrees. Moreover, a 5ʹ-G induces domain motions in the protein, which trigger a previously unreported interaction between the 51-base and the L2 linker domain. Combining our in silico analyses with biochemical studies of recombinant hAgo2, we find that, contrary to previous observations, hAgo2 is capable of functionally accommodating guide strands regardless of the 5ʹ-base

Using machine-learning-driven approaches to boost hot-spot's knowledge

Understanding protein–protein interactions (PPIs) is fundamental to describe and to characterize the formation of biomolecular assemblies, and to establish the energetic principles underlying biological networks. One key aspect of these interfaces is the existence and prevalence of hot-spots (HS) residues that, upon mutation to alanine, negatively impact the formation of such protein–protein complexes. HS have been widely considered in research, both in case studies and in a few large-scale predictive approaches. This review aims to present the current knowledge on PPIs, providing a detailed understanding of the microspecifications of the residues involved in those interactions and the characteristics of those defined as HS through a thorough assessment of related field-specific methodologies. We explore recent accurate artificial intelligence-based techniques, which are progressively replacing well-established classical energy-based methodologies. This article is categorized under: Data Science > Databases and Expert Systems Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions

Cyclization and Docking Protocol for Cyclic Peptide-Protein Modeling Using HADDOCK2.4

An emerging class of therapeutic molecules are cyclic peptides with over 40 cyclic peptide drugs currently in clinical use. Their mode of action is, however, not fully understood, impeding rational drug design. Computational techniques could positively impact their design, but modeling them and their interactions remains challenging due to their cyclic nature and their flexibility. This study presents a step-by-step protocol for generating cyclic peptide conformations and docking them to their protein target using HADDOCK2.4. A dataset of 30 cyclic peptide-protein complexes was used to optimize both cyclization and docking protocols. It supports peptides cyclized via an N- and C-terminus peptide bond and/or a disulfide bond. An ensemble of cyclic peptide conformations is then used in HADDOCK to dock them onto their target protein using knowledge of the binding site on the protein side to drive the modeling. The presented protocol predicts at least one acceptable model according to the critical assessment of prediction of interaction criteria for each complex of the dataset when the top 10 HADDOCK-ranked single structures are considered (100% success rate top 10) both in the bound and unbound docking scenarios. Moreover, its performance in both bound and fully unbound docking is similar to the state-of-the-art software in the field, Autodock CrankPep. The presented cyclization and docking protocol should make HADDOCK a valuable tool for rational cyclic peptide-based drug design and high-throughput screening

Molecular Insights Into Binding and Activation of the Human KCNQ2 Channel by Retigabine

Voltage-gated potassium channels of the Kv7.x family are involved in a plethora of biological processes across many tissues in animals, and their misfunctioning could lead to several pathologies ranging from diseases caused by neuronal hyperexcitability, such as epilepsy, or traumatic injuries and painful diabetic neuropathy to autoimmune disorders. Among the members of this family, the Kv7.2 channel can form hetero-tetramers together with Kv7.3, forming the so-called M-channels, which are primary regulators of intrinsic electrical properties of neurons and of their responsiveness to synaptic inputs. Here, prompted by the similarity between the M-current and that in Kv7.2 alone, we perform a computational-based characterization of this channel in its different conformational states and in complex with the modulator retigabine. After validation of the structural models of the channel by comparison with experimental data, we investigate the effect of retigabine binding on the two extreme states of Kv7.2 (resting-closed and activated-open). Our results suggest that binding, so far structurally characterized only in the intermediate activated-closed state, is possible also in the other two functional states. Moreover, we show that some effects of this binding, such as increased flexibility of voltage sensing domains and propensity of the pore for open conformations, are virtually independent on the conformational state of the protein. Overall, our results provide new structural and dynamic insights into the functioning and the modulation of Kv7.2 and related channels

Unveiling the interaction of vanadium compounds with human serum albumin by using 1H STD NMR and computational docking studies

The binding of the VV oxidation products of two vanadium(IV) compounds, [VO(dmpp)2] and [VO(maltolato)2], which have shown promising anti-diabetic properties, to human serum albumin (HSA) in aqueous aerobic solution has been studied by 1H saturation transfer difference (STD) NMR spectroscopy and computational docking studies. Group epitope mapping and docking simulations indicate a preference of HSA binding to the 1:1 [VO2(dmpp)(OH)(H2O)]- and 1:2 [VO 2(maltol)2]- vanadium(V) species. By using known HSA binders, competition NMR experiments revealed that both complexes preferentially bind to drug site I. Docking simulations carried out with HADDOCK together with restraints derived from the STD results led to three-dimensional models that are in agreement with the NMR spectroscopic data, providing useful information on molecular interaction modes. These results indicate that the combination of STD NMR and data-driven docking is a good tool for elucidating the interactions in protein-vanadium compounds and thus for clarifying the mechanism of drug delivery as vanadium compounds have shown potential therapeutic properties. 1H STD NMR analysis complemented by HADDOCK studies have revealed that the [VO2(dmpp)(H2O)(OH)] - species, resulting from the oxidation of the potential insulin mimetic VO(dmpp)2, binds preferentially to HSA site I. These findings corroborate the involvement of this serum protein in the transport of vanadium species in the blood stream and their delivery to target cells

Sharing data from molecular simulations

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 (https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations

Teixobactin kills bacteria by a two-pronged attack on the cell envelope

Antibiotics that use novel mechanisms are needed to combat antimicrobial resistance1–3. Teixobactin4 represents a new class of antibiotics with a unique chemical scaffold and lack of detectable resistance. Teixobactin targets lipid II, a precursor of peptidoglycan5. Here we unravel the mechanism of teixobactin at the atomic level using a combination of solid-state NMR, microscopy, in vivo assays and molecular dynamics simulations. The unique enduracididine C-terminal headgroup of teixobactin specifically binds to the pyrophosphate-sugar moiety of lipid II, whereas the N terminus coordinates the pyrophosphate of another lipid II molecule. This configuration favours the formation of a β-sheet of teixobactins bound to the target, creating a supramolecular fibrillar structure. Specific binding to the conserved pyrophosphate-sugar moiety accounts for the lack of resistance to teixobactin4. The supramolecular structure compromises membrane integrity. Atomic force microscopy and molecular dynamics simulations show that the supramolecular structure displaces phospholipids, thinning the membrane. The long hydrophobic tails of lipid II concentrated within the supramolecular structure apparently contribute to membrane disruption. Teixobactin hijacks lipid II to help destroy the membrane. Known membrane-acting antibiotics also damage human cells, producing undesirable side effects. Teixobactin damages only membranes that contain lipid II, which is absent in eukaryotes, elegantly resolving the toxicity problem. The two-pronged action against cell wall synthesis and cytoplasmic membrane produces a highly effective compound targeting the bacterial cell envelope. Structural knowledge of the mechanism of teixobactin will enable the rational design of improved drug candidates

Teixobactin kills bacteria by a two-pronged attack on the cell envelope

Antibiotics that use novel mechanisms are needed to combat antimicrobial resistance1–3. Teixobactin4 represents a new class of antibiotics with a unique chemical scaffold and lack of detectable resistance. Teixobactin targets lipid II, a precursor of peptidoglycan5. Here we unravel the mechanism of teixobactin at the atomic level using a combination of solid-state NMR, microscopy, in vivo assays and molecular dynamics simulations. The unique enduracididine C-terminal headgroup of teixobactin specifically binds to the pyrophosphate-sugar moiety of lipid II, whereas the N terminus coordinates the pyrophosphate of another lipid II molecule. This configuration favours the formation of a β-sheet of teixobactins bound to the target, creating a supramolecular fibrillar structure. Specific binding to the conserved pyrophosphate-sugar moiety accounts for the lack of resistance to teixobactin4. The supramolecular structure compromises membrane integrity. Atomic force microscopy and molecular dynamics simulations show that the supramolecular structure displaces phospholipids, thinning the membrane. The long hydrophobic tails of lipid II concentrated within the supramolecular structure apparently contribute to membrane disruption. Teixobactin hijacks lipid II to help destroy the membrane. Known membrane-acting antibiotics also damage human cells, producing undesirable side effects. Teixobactin damages only membranes that contain lipid II, which is absent in eukaryotes, elegantly resolving the toxicity problem. The two-pronged action against cell wall synthesis and cytoplasmic membrane produces a highly effective compound targeting the bacterial cell envelope. Structural knowledge of the mechanism of teixobactin will enable the rational design of improved drug candidates