POPS: A fast algorithm for solvent accessible surface areas at atomic and residue level

Quantitation of oxidized and reduced albumin in mammals. An intriguing analytical question

Oxidized albumin is considered a short-term biomarker of oxidative stress and its measurement in blood contributes to evaluate the impact of diseases, drugs, dialytic treatments, physical activity, environmental contaminants etc. on the red-ox balance of humans as well as of other mammalians. Nevertheless, the most common methods for quantifying the oxidized and reduced albumins are costly and time-consuming. Furthermore, there is a dearth of information regarding the proper ways to store human serum or plasma samples in order to prevent inaccurate quantification of these various albumin forms. This paper explores these aspects and proposes a few spectrophotometric assay procedures which make the quantitation of oxidized and reduced albumin very fast, precise and un-expensive in various mammals.

Molecular Origins of the Mendelian Rare Diseases Reviewed by Orpha.net: A Structural Bioinformatics Investigation

The study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction methods provides the opportunity to investigate the molecular origins of most of the rare diseases reviewed in the Orpha.net database. Thus, it has been possible to analyze the topology of the pathogenic missense variants found in the 2515 proteins involved in Mendelian rare diseases (MRDs), which form the database for our structural bioinformatics study. The amino acid substitutions responsible for MRDs showed different mutation site distributions at different three-dimensional protein depths. We then highlighted the depth-dependent effects of pathogenic variants for the 20,061 pathogenic variants that are present in our database. The results of this structural bioinformatics investigation are relevant, as they provide additional clues to mitigate the damage caused by MRD.

Accurate Protein pKa Prediction with Physical Organic Chemistry Guided 3D Protein Representation

Protein pKa is a fundamental physicochemical parameter that dictates protein structure and function. However, accurately determining protein site-pKa values remains a substantial challenge, both experimentally and theoretically. In this study, we introduce a physical organic approach, leveraging a protein structural and physical-organic-parameter-based representation (P-SPOC), to develop a rapid and intuitive model for protein pKa prediction. Our P-SPOC model achieves state-of-the-art predictive accuracy, with a mean absolute error (MAE) of 0.33 pKa units. Furthermore, we have incorporated advanced protein structure prediction models, like AlphaFold2, to approximate structures for proteins lacking three-dimensional representations, which enhances the applicability of our model in the context of structure-undetermined protein research. To promote broader accessibility within the research community, an online prediction interface was also established at isyn.luoszgroup.com.

A computational odyssey: uncovering classical β-lactamase inhibitors in dry fruits

In the antibacterial arsenal, β-lactams have held a prominent position, but increasing resistance due to unauthorized use and genetic factors requires new strategies. Combining β-lactamase inhibitors with broad-spectrum β-lactams proves effective in combating this resistance. ESBL producers demand new inhibitors, leading to the exploration of plant-derived secondary metabolites for potent β-lactam antibiotics or alternative inhibitors. Using virtual screening, molecular docking, ADMET analysis, and molecular dynamic simulation, this study actively analyzed the inhibitory activity of figs, cashews, walnuts, and peanuts against SHV-1, NDM-1, KPC-2, and OXA-48 β-lactamases. Using AutoDock Vina, the docking affinities of various compounds for target enzymes were initially screened, revealing 12 bioactive compounds with higher affinities for the target enzymes compared to Avibactam and Tazobactam. Top-scoring metabolites, including Oleanolic acid, Protocatechuic acid, and Tannin, were subjected to MD simulation studies to further analyze the stability of the docked complexes using WebGro. The simulation coordinates, in terms of RMSD, RMSF, SASA, Rg, and hydrogen bonds formed, showed that these phytocompounds are stable enough to retain in the active sites at various orientations. The PCA and FEL analysis also showed the stability of the dynamic motion of Cα residues of phytochemical-bound enzymes. The pharmacokinetic analysis of the top phytochemicals was performed to analyze their bioavailability and toxicity. This study provides new insights into the therapeutic potential of phytochemicals of selected dry fruits and contributes to future experimental studies to identify βL inhibitors from plants.Communicated by Ramaswamy H. Sarma.

Assessing the functional impact of protein binding site definition

Many biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data, this approach also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also shows that methods for protein binding interface predictions should perform above approximately F-score = 0.7 accuracy level to capture the biological function of a protein.

Antibodies from dengue patients with prior exposure to Japanese encephalitis virus are broadly neutralizing against Zika virus

Exposure to multiple mosquito-borne flaviviruses within a lifetime is not uncommon; however, how sequential exposures to different flaviviruses shape the cross-reactive humoral response against an antigen from a different serocomplex has yet to be explored. Here, we report that dengue-infected individuals initially primed with the Japanese encephalitis virus (JEV) showed broad, highly neutralizing potencies against Zika virus (ZIKV). We also identified a rare class of ZIKV-cross-reactive human monoclonal antibodies with increased somatic hypermutation and broad neutralization against multiple flaviviruses. One huMAb, K8b, binds quaternary epitopes with heavy and light chains separately interacting with overlapping envelope protein dimer units spanning domains I, II, and III through cryo-electron microscopy and structure-based mutagenesis. JEV virus-like particle immunization in mice further confirmed that such cross-reactive antibodies, mainly IgG3 isotype, can be induced and proliferate through heterologous dengue virus (DENV) serotype 2 virus-like particle stimulation. Our findings highlight the role of prior immunity in JEV and DENV in shaping the breadth of humoral response and provide insights for future vaccination strategies in flavivirus-endemic countries.

Multiple sequence alignment-based RNA language model and its application to structural inference

Compared with proteins, DNA and RNA are more difficult languages to interpret because four-letter coded DNA/RNA sequences have less information content than 20-letter coded protein sequences. While BERT (Bidirectional Encoder Representations from Transformers)-like language models have been developed for RNA, they are ineffective at capturing the evolutionary information from homologous sequences because unlike proteins, RNA sequences are less conserved. Here, we have developed an unsupervised multiple sequence alignment-based RNA language model (RNA-MSM) by utilizing homologous sequences from an automatic pipeline, RNAcmap, as it can provide significantly more homologous sequences than manually annotated Rfam. We demonstrate that the resulting unsupervised, two-dimensional attention maps and one-dimensional embeddings from RNA-MSM contain structural information. In fact, they can be directly mapped with high accuracy to 2D base pairing probabilities and 1D solvent accessibilities, respectively. Further fine-tuning led to significantly improved performance on these two downstream tasks compared with existing state-of-the-art techniques including SPOT-RNA2 and RNAsnap2. By comparison, RNA-FM, a BERT-based RNA language model, performs worse than one-hot encoding with its embedding in base pair and solvent-accessible surface area prediction. We anticipate that the pre-trained RNA-MSM model can be fine-tuned on many other tasks related to RNA structure and function.

Analyzing the Geometry and Dynamics of Viral Structures: A Review of Computational Approaches Based on Alpha Shape Theory, Normal Mode Analysis, and Poisson-Boltzmann Theories

The current SARS-CoV-2 pandemic highlights our fragility when we are exposed to emergent viruses either directly or through zoonotic diseases. Fortunately, our knowledge of the biology of those viruses is improving. In particular, we have more and more structural information on virions, i.e., the infective form of a virus that includes its genomic material and surrounding protective capsid, and on their gene products. It is important to have methods that enable the analyses of structural information on such large macromolecular systems. We review some of those methods in this paper. We focus on understanding the geometry of virions and viral structural proteins, their dynamics, and their energetics, with the ambition that this understanding can help design antiviral agents. We discuss those methods in light of the specificities of those structures, mainly that they are huge. We focus on three of our own methods based on the alpha shape theory for computing geometry, normal mode analyses to study dynamics, and modified Poisson-Boltzmann theories to study the organization of ions and co-solvent and solvent molecules around biomacromolecules. The corresponding software has computing times that are compatible with the use of regular desktop computers. We show examples of their applications on some outer shells and structural proteins of the West Nile Virus.

Assessing the functional impact of protein binding site definition

Many biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data this approach generates, it also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also provides guidance on the minimum expected accuracy of interface definition that is required to capture the biological function of a protein.

Computing the Volume, Surface Area, Mean, and Gaussian Curvatures of Molecules and Their Derivatives

Geometry is crucial in our efforts to comprehend the structures and dynamics of biomolecules. For example, volume, surface area, and integrated mean and Gaussian curvature of the union of balls representing a molecule are used to quantify its interactions with the water surrounding it in the morphometric implicit solvent models. The Alpha Shape theory provides an accurate and reliable method for computing these geometric measures. In this paper, we derive homogeneous formulas for the expressions of these measures and their derivatives with respect to the atomic coordinates, and we provide algorithms that implement them into a new software package, AlphaMol. The only variables in these formulas are the interatomic distances, making them insensitive to translations and rotations. AlphaMol includes a sequential algorithm and a parallel algorithm. In the parallel version, we partition the atoms of the molecule of interest into 3D rectangular blocks, using a kd-tree algorithm. We then apply the sequential algorithm of AlphaMol to each block, augmented by a buffer zone to account for atoms whose ball representations may partially cover the block. The current parallel version of AlphaMol leads to a 20-fold speed-up compared to an independent serial implementation when using 32 processors. For instance, it takes 31 s to compute the geometric measures and derivatives of each atom in a viral capsid with more than 26 million atoms on 32 Intel processors running at 2.7 GHz. The presence of the buffer zones, however, leads to redundant computations, which ultimately limit the impact of using multiple processors. AlphaMol is available as an OpenSource software.

Predicting RNA solvent accessibility from multi-scale context feature via multi-shot neural network

Knowledge of RNA solvent accessibility has recently become attractive due to the increasing awareness of its importance for key biological process. Accurately predicting the solvent accessibility of RNA is crucial for understanding its 3D structure and biological function. In this study, we develop a novel computational method, termed M²pred, for accurately predicting the solvent accessibility of RNA from sequence-based multi-scale context feature. In M²pred, three single-view features, i.e., base-pairing probabilities, position-specific frequency matrix, and a binary one-hot encoding, are first generated as three feature sources, and immediately concatenated to engender a super feature. Secondly, for the super feature, the matrix-format features of each nucleotide are extracted using an initialized sliding window technique, and regularly stacked into a cube-format feature. Then, using multi-scale context feature extraction strategy, a pyramid feature constructed of contextual feature of four scales related to target nucleotides is extracted from the cube-format feature. Finally, a customized multi-shot neural network framework, which is equipped with four different scales of receptive fields mainly integrating several residual attention blocks, is designed to dig discrimination information from the contextual pyramid feature. Experimental results demonstrate that the proposed M²pred achieve a high prediction performance and outperforms existing state-of-the-art prediction methods of RNA solvent accessibility.

Energy Landscapes for Base-Flipping in a Model DNA Duplex

We explore the process of base-flipping for four central bases, adenine, guanine, cytosine, and thymine, in a deoxyribonucleic acid (DNA) duplex using the energy landscape perspective. NMR imino-proton exchange and fluorescence correlation spectroscopy studies have been used in previous experiments to obtain lifetimes for bases in paired and extrahelical states. However, the difference of almost 4 orders of magnitude in the base-flipping rates obtained by the two methods implies that they are exploring different pathways and possibly different open states. Our results support the previous suggestion that minor groove opening may be favored by distortions in the DNA backbone and reveal links between sequence effects and the direction of opening, i.e., whether the base flips toward the major or the minor groove side. In particular, base flipping along the minor groove pathway was found to align toward the 5' side of the backbone. We find that bases align toward the 3' side of the backbone when flipping along the major groove pathway. However, in some cases for cytosine and thymine, the base flipping along the major groove pathway also aligns toward the 5' side. The sequence effect may be caused by the polar interactions between the flipping-base and its neighboring bases on either of the strands. For guanine flipping toward the minor groove side, we find that the equilibrium constant for opening is large compared to flipping via the major groove. We find that the estimated rates of base opening, and hence the lifetimes of the closed state, obtained for thymine flipping through small and large angles along the major groove differ by 6 orders of magnitude, whereas for thymine flipping through small angles along the minor groove and large angles along the major groove, the rates differ by 3 orders of magnitude.

Spotlight onto surfactant-steam-bitumen interfacial behavior via molecular dynamics simulation

Heavy oil and bitumen play a vital role in the global energy supply, and to unlock such resources, thermal methods, e.g., steam injection, are applied. To improve the performance of these methods, different additives, such as air, solvents, and chemicals, can be used. As a subset of chemicals, surfactants are one of the potential additives for steam-based bitumen recovery methods. Molecular interactions between surfactant/steam/bitumen have not been addressed in the literature. This paper investigates molecular interactions between anionic surfactants, steam, and bitumen in high-temperature and high-pressure conditions. For this purpose, a real Athabasca oil sand composition is employed to assess the phase behavior of surfactant/steam/bitumen under in-situ steam-based bitumen recovery. Two different asphaltene architectures, archipelago and Island, are used to examine the effect of asphaltene type on bitumen's interfacial behavior. The influence of having sulfur heteroatoms in a resin structure and a benzene ring's effect in an anionic surfactant structure on surfactant-steam-bitumen interactions are investigated systematically. The outputs are supported by different analyses, including radial distribution functions (RDFs), mean squared displacement (MSD), radius of gyration, self-diffusion coefficient, solvent accessible surface area (SASA), interfacial thickness, and interaction energies. According to MD outputs, adding surfactant molecules to the steam phase improved the interaction energy between steam and bitumen. Moreover, surfactants can significantly improve steam emulsification capability by decreasing the interfacial tension (IFT) between bitumen and the steam phase. Asphaltene architecture has a considerable effect on the interfacial behavior in such systems. This study provides a better and more in-depth understanding of surfactant-steam-bitumen systems and spotlights the interactions between bitumen fractions and surfactant molecules under thermal recovery conditions.

The anatomy of unfolding of Yfh1 is revealed by site-specific fold stability analysis measured by 2D NMR spectroscopy

Most techniques allow detection of protein unfolding either by following the behaviour of single reporters or as an averaged all-or-none process. We recently added 2D NMR spectroscopy to the well-established techniques able to obtain information on the process of unfolding using resonances of residues in the hydrophobic core of a protein. Here, we questioned whether an analysis of the individual stability curves from each resonance could provide additional site-specific information. We used the Yfh1 protein that has the unique feature to undergo both cold and heat denaturation at temperatures above water freezing at low ionic strength. We show that stability curves inconsistent with the average NMR curve from hydrophobic core residues mainly comprise exposed outliers that do nevertheless provide precious information. By monitoring both cold and heat denaturation of individual residues we gain knowledge on the process of cold denaturation and convincingly demonstrate that the two unfolding processes are intrinsically different.

RNA Flexibility Prediction With Sequence Profile and Predicted Solvent Accessibility

Structural flexibility plays an essential role in many biological processes. B-factor is an important indicator to measure the flexibility of protein or RNA structures. Many methods were developed to predict protein B-factors, but few studies have been done for RNA B-factor prediction. In this paper, we proposed a new method RNAbval to predict RNA B-factors using random forest. The method was developed using a comprehensive set of features, including the sequence profile and predicted solvent accessibility. RNAbval achieved an improvement of 9.2-20.5 percent over the state-of-the-art method on two benchmark test datasets. The proposed method is available at http://yanglab.nankai.edu.cn/RNAbval/.

Accurate Estimation of Solvent Accessible Surface Area for Coarse-Grained Biomolecular Structures with Deep Learning

Coarse-grained (CG) models of biomolecules have been widely used in protein/ribonucleic acid (RNA) three-dimensional structure prediction, docking, drug design, and molecular simulations due to their superiority in computational efficiency. Most of these applications strongly depend on the reasonable estimation of solvation free energy, which requires the accurate calculation of solvent accessible surface area (SASA). Although algorithms for SASA calculations with all-atom protein and RNA structures have been well-established, accurately estimating the SASA based on CG structures is extremely challenging. In this work, we developed a deep learning-based SASA estimator (DeepCGSA), which can provide almost perfect SASA estimation based on CG structures of protein and RNA molecules. Extensive testing analysis showed that for three types of widely used CG protein models, including the Cα-based, Cα-Cβ, and Martini models, the correlation coefficients between the predicted values and the reference values can be as high as 0.95-0.99, which perform dramatically better than available methods. In addition, the new method can be used for CG RNA structures and unfolded protein structures with much improved accuracy. We anticipate that DeepCGSA will be highly useful in the protein/RNA structure prediction, drug design, and other applications, in which accurate estimations of SASA for CG biomolecular structures are critically important.

Gastrobodies are engineered antibody mimetics resilient to pepsin and hydrochloric acid

Protein-based targeting reagents, such as antibodies and non-antibody scaffold proteins, are rapidly inactivated in the upper gastrointestinal (GI) tract. Hydrochloric acid in gastric juice denatures proteins and activates pepsin, concentrations of which reach 1 mg/mL in the mammalian stomach. Two stable scaffold proteins (nanobody and nanofitin), previously developed to be protease-resistant, were completely digested in less than 10 min at 100-fold lower concentration of pepsin than found in the stomach. Here we present gastrobodies, a protein scaffold derived from Kunitz soybean trypsin inhibitor (SBTI). SBTI is highly resistant to the challenges of the upper GI tract, including digestive proteases, pH 2 and bile acids. Computational prediction of SBTI's evolvability identified two nearby loops for randomization, to create a potential recognition surface which was experimentally validated by alanine scanning. We established display of SBTI on full-length pIII of M13 phage. Phage selection of gastrobody libraries against the glucosyltransferase domain of Clostridium difficile toxin B (GTD) identified hits with nanomolar affinity and enzyme inhibitory activity. Anti-GTD binders retained high stability to acid, digestive proteases and heat. Gastrobodies show resilience to exceptionally harsh conditions, which should provide a foundation for targeting and modulating function within the GI tract.

Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network

MOTIVATION

RNA solvent accessibility, similar to protein solvent accessibility, reflects the structural regions that are accessible to solvents or other functional biomolecules, and plays an important role for structural and functional characterization. Unlike protein solvent accessibility, only a few tools are available for predicting RNA solvent accessibility despite the fact that millions of RNA transcripts have unknown structures and functions. Also, these tools have limited accuracy. Here, we have developed RNAsnap2 that uses a dilated convolutional neural network with a new feature, based on predicted base-pairing probabilities from LinearPartition.

RESULTS

Using the same training set from the recent predictor RNAsol, RNAsnap2 provides an 11% improvement in median Pearson Correlation Coefficient (PCC) and 9% improvement in mean absolute errors for the same test set of 45 RNA chains. A larger improvement (22% in median PCC) is observed for 31 newly deposited RNA chains that are non-redundant and independent from the training and the test sets. A single-sequence version of RNAsnap2 (i.e. without using sequence profiles generated from homology search by Infernal) has achieved comparable performance to the profile-based RNAsol. In addition, RNAsnap2 has achieved comparable performance for protein-bound and protein-free RNAs. Both RNAsnap2 and RNAsnap2 (SingleSeq) are expected to be useful for searching structural signatures and locating functional regions of non-coding RNAs.

AVAILABILITY AND IMPLEMENTATION

Standalone-versions of RNAsnap2 and RNAsnap2 (SingleSeq) are available at https://github.com/jaswindersingh2/RNAsnap2. Direct prediction can also be made at https://sparks-lab.org/server/rnasnap2. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Pathogenic missense protein variants affect different functional pathways and proteomic features than healthy population variants

Missense variants are present amongst the healthy population, but some of them are causative of human diseases. A classification of variants associated with “healthy” or “diseased” states is therefore not always straightforward. A deeper understanding of the nature of missense variants in health and disease, the cellular processes they may affect, and the general molecular principles which underlie these differences is essential to offer mechanistic explanations of the true impact of pathogenic variants. Here, we have formalised a statistical framework which enables robust probabilistic quantification of variant enrichment across full-length proteins, their domains, and 3D structure-defined regions. Using this framework, we validate and extend previously reported trends of variant enrichment in different protein structural regions (surface/core/interface). By examining the association of variant enrichment with available functional pathways and transcriptomic and proteomic (protein half-life, thermal stability, abundance) data, we have mined a rich set of molecular features which distinguish between pathogenic and population variants: Pathogenic variants mainly affect proteins involved in cell proliferation and nucleotide processing and are enriched in more abundant proteins. Additionally, rare population variants display features closer to common than pathogenic variants. We validate the association between these molecular features and variant pathogenicity by comparing against existing in silico variant impact annotations. This study provides molecular details into how different proteins exhibit resilience and/or sensitivity towards missense variants and provides the rationale to prioritise variant-enriched proteins and protein domains for therapeutic targeting and development. The ZoomVar database, which we created for this study, is available at fraternalilab.kcl.ac.uk/ZoomVar. It allows users to programmatically annotate missense variants with protein structural information and to calculate variant enrichment in different protein structural regions.

How do can one improve the classification of genetic variants as harmful or harmless? This study uses a robust statistical analysis to exploit the interplay between protein structure, proteomic measurements and functional pathways to enable better discrimination between missense variants in health and disease.

CS-Annotate: A Tool for Using NMR Chemical Shifts to Annotate RNA Structure

Here, we introduce CS-Annotate, a tool that uses assigned NMR chemical shifts to annotate structural features in RNA. At its core, CS-Annotate is a deployment of a multitask deep learning model that simultaneously classifies the solvent exposure, base-stacking and -pairing status, and conformation of individual RNA residues from their chemical shift fingerprint. Here, we briefly describe how we trained and tested the classifier and demonstrate its application to a model RNA system. CS-Annotate can be accessed via the SMALTR (Structure-based MAchine Learning Tools for RNA) Science Gateway (smaltr.org).

Making sense of missense variants in TTN-related congenital myopathies

Mutations in the sarcomeric protein titin, encoded by TTN, are emerging as a common cause of myopathies. The diagnosis of a TTN-related myopathy is, however, often not straightforward due to clinico-pathological overlap with other myopathies and the prevalence of TTN variants in control populations. Here, we present a combined clinico-pathological, genetic and biophysical approach to the diagnosis of TTN-related myopathies and the pathogenicity ascertainment of TTN missense variants. We identified 30 patients with a primary TTN-related congenital myopathy (CM) and two truncating variants, or one truncating and one missense TTN variant, or homozygous for one TTN missense variant. We found that TTN-related myopathies show considerable overlap with other myopathies but are strongly suggested by a combination of certain clinico-pathological features. Presentation was typically at birth with the clinical course characterized by variable progression of weakness, contractures, scoliosis and respiratory symptoms but sparing of extraocular muscles. Cardiac involvement depended on the variant position. Our biophysical analyses demonstrated that missense mutations associated with CMs are strongly destabilizing and exert their effect when expressed on a truncating background or in homozygosity. We hypothesise that destabilizing TTN missense mutations phenocopy truncating variants and are a key pathogenic feature of recessive titinopathies that might be amenable to therapeutic intervention.

WIDOCK: a reactive docking protocol for virtual screening of covalent inhibitors

Here we present WIDOCK, a virtual screening protocol that supports the selection of diverse electrophiles as covalent inhibitors by incorporating ligand reactivity towards cysteine residues into AutoDock4. WIDOCK applies the reactive docking method (Backus et al. in Nature 534:570–574, 2016) and extends it into a virtual screening tool by introducing facile experimental or computational parametrization and a ligand focused evaluation scheme together with a retrospective and prospective validation against various therapeutically relevant targets. Parameters accounting for ligand reactivity are derived from experimental reaction kinetic data or alternatively from computed reaction barriers. The performance of this docking protocol was first evaluated by investigating compound series with diverse warhead chemotypes against KRAS^G12C, MurA and cathepsin B. In addition, WIDOCK was challenged on larger electrophilic libraries screened against OTUB2 and NUDT7. These retrospective analyses showed high sensitivity in retrieving experimental actives, by also leading to superior ROC curves, AUC values and better enrichments than the standard covalent docking tool available in AutoDock4 when compound collections with diverse warheads were investigated. Finally, we applied WIDOCK for the prospective identification of covalent human MAO-A inhibitors acting via a new mechanism by binding to Cys323. The inhibitory activity of several predicted compounds was experimentally confirmed and the labelling of Cys323 was proved by subsequent MS/MS measurements. These findings demonstrate the usefulness of WIDOCK as a warhead-sensitive, covalent virtual screening protocol.

Conformational Changes of Thyroid Receptors in Response to Antagonists

Thyroid hormone receptors (TRs) play a critical role in human development, growth, and metabolism. Antagonists of TRs offer an attractive strategy to treat hyperthyroidism without the disadvantage of a delayed onset of drug action. While it is challenging to examine the atomistic behavior of TRs in a laboratory setting, computational methods such as molecular dynamics (MD) simulations have proven their value to elucidate ligand-induced conformational changes in nuclear receptors. Here, we performed MD simulations of TRα and TRβ complexed to their native ligand triiodothyronine (T3) as well as several antagonists. Based on the examination of 27 μs MD trajectories, we showed how binding of these compounds influences various structural features of the receptors including the helicity of helices 3 and 10 as well as the location of helix-12. Helices 3 and 12 are known to mediate coactivator association required for downstream signaling, suggesting these changes to be the molecular basis for TR antagonism. A mechanistic analysis of the trajectories revealed an allosteric pathway between H3 and H12 to be responsible for the conformational adaptations. Even though a mechanistic understanding of conformational adaptations triggered by TR antagonists is important for the development of novel therapeutics, they have not been previously examined in detail as it was done here.

Dissecting the Conformational Dynamics of the Bile Acid Transporter Homologue ASBTNM

Apical sodium-dependent bile acid transporter (ASBT) catalyses uphill transport of bile acids using the electrochemical gradient of Na⁺ as the driving force. The crystal structures of two bacterial homologues ASBT_NM and ASBT_Yf have previously been determined, with the former showing an inward-facing conformation, and the latter adopting an outward-facing conformation accomplished by the substitution of the critical Na⁺-binding residue glutamate-254 with an alanine residue. While the two crystal structures suggested an elevator-like movement to afford alternating access to the substrate binding site, the mechanistic role of Na⁺ and substrate in the conformational isomerization remains unclear. In this study, we utilized site-directed alkylation monitored by in-gel fluorescence (SDAF) to probe the solvent accessibility of the residues lining the substrate permeation pathway of ASBT_NM under different Na⁺ and substrate conditions, and interpreted the conformational states inferred from the crystal structures. Unexpectedly, the crosslinking experiments demonstrated that ASBT_NM is a monomer protein, unlike the other elevator-type transporters, usually forming a homodimer or a homotrimer. The conformational dynamics observed by the biochemical experiments were further validated using DEER measuring the distance between the spin-labelled pairs. Our results revealed that Na⁺ ions shift the conformational equilibrium of ASBT_NM toward the inward-facing state thereby facilitating cytoplasmic uptake of substrate. The current findings provide a novel perspective on the conformational equilibrium of secondary active transporters.

Recognition of small molecule-RNA binding sites using RNA sequence and structure

Motivation

RNA molecules become attractive small molecule drug targets to treat disease in recent years. Computer-aided drug design can be facilitated by detecting the RNA sites that bind small molecules. However, very limited progress has been reported for the prediction of small molecule–RNA binding sites.

Results

We developed a novel method RNAsite to predict small molecule–RNA binding sites using sequence profile- and structure-based descriptors. RNAsite was shown to be competitive with the state-of-the-art methods on the experimental structures of two independent test sets. When predicted structure models were used, RNAsite outperforms other methods by a large margin. The possibility of improving RNAsite by geometry-based binding pocket detection was investigated. The influence of RNA structure’s flexibility and the conformational changes caused by ligand binding on RNAsite were also discussed. RNAsite is anticipated to be a useful tool for the design of RNA-targeting small molecule drugs.

Availability and implementation

http://yanglab.nankai.edu.cn/RNAsite.

Supplementary information

Supplementary data are available at Bioinformatics online.

Bionoi: A Voronoi Diagram-Based Representation of Ligand-Binding Sites in Proteins for Machine Learning Applications

Bionoi is a new software to generate Voronoi representations of ligand-binding sites in proteins for machine learning applications. Unlike many other deep learning models in biomedicine, Bionoi utilizes off-the-shelf convolutional neural network architectures, reducing the development work without sacrificing the performance. When initially generating images of binding sites, users have the option to color the Voronoi cells based on either one of six structural, physicochemical, and evolutionary properties, or a blend of all six individual properties. Encouragingly, after inputting images generated by Bionoi into the convolutional autoencoder, the network was able to effectively learn the most salient features of binding pockets. The accuracy of the generated model is evaluated both visually and numerically through the reconstruction of binding site images from the latent feature space. The generated feature vectors capture well various properties of binding sites and thus can be applied in a multitude of machine learning projects. As a demonstration, we trained the ResNet-18 architecture from Microsoft on Bionoi images to show that it is capable to effectively classify nucleotide- and heme-binding pockets against a large dataset of control pockets binding a variety of small molecules. Bionoi is freely available to the research community at https://github.com/CSBG-LSU/BionoiNet.

Allosteric Priming of E. coli CheY by the Flagellar Motor Protein FliM

Phosphorylation of Escherichia coli CheY protein transduces chemoreceptor stimulation to a highly cooperative flagellar motor response. CheY binds to the N-terminal peptide of the FliM motor protein (FliM_N). Constitutively active D13K-Y106W CheY has been an important tool for motor physiology. The crystal structures of CheY and CheY ⋅ FliM_N with and without D13K-Y106W have shown FliM_N-bound CheY contains features of both active and inactive states. We used molecular dynamics (MD) simulations to characterize the CheY conformational landscape accessed by FliM_N and D13K-Y106W. Mutual information measures identified the central features of the long-range CheY allosteric network between D57 phosphorylation site and the FliM_N interface, namely the closure of the α4-β4 hinge and inward rotation of Y- or W106 with W58. We used hydroxy-radical foot printing with mass spectroscopy (XFMS) to track the solvent accessibility of these and other side chains. The solution XFMS oxidation rate correlated with the solvent-accessible area of the crystal structures. The protection of allosteric relay side chains reported by XFMS confirmed the intermediate conformation of the native CheY ⋅ FliM_N complex, the inactive state of free D13K-Y106W CheY, and the MD-based network architecture. We extended the MD analysis to determine temporal coupling and energetics during activation. Coupled aromatic residue rotation was a graded rather than a binary switch, with Y- or W106 side-chain burial correlated with increased FliM_N affinity. Activation entrained CheY fold stabilization to FliM_N affinity. The CheY network could be partitioned into four dynamically coordinated sectors. Residue substitutions mapped to sectors around D57 or the FliM_N interface according to phenotype. FliM_N increased sector size and interactions. These sectors fused between the substituted K13-W106 residues to organize a tightly packed core and novel surfaces that may bind additional sites to explain the cooperative motor response. The community maps provide a more complete description of CheY priming than proposed thus far.

Understanding the structural details of APOBEC3-DNA interactions using graph-based representations

Human APOBEC3 (A3; apolipoprotein B mRNA editing catalytic polypeptide-like 3) is a family of seven enzymes involved in generating mutations in nascent reverse transcripts of many retroviruses, as well as the human genome in a range of cancer types. The structural details of the interaction between A3 proteins and DNA molecules are only available for a few family members. Here we use homology modelling techniques to address the difference in structural coverage of human A3 enzymes interacting with different DNA substrates. A3-DNA interfaces are represented as residue networks ("graphs"), based on which features at these interfaces are compared and quantified. We demonstrate that graph-based representations are effective in highlighting structural features of A3-DNA interfaces. By large-scale in silico mutagenesis of the bound DNA chain, we predicted the preference of substrate DNA sequence for multiple A3 domains. These data suggested that computational modelling approaches could contribute in the exploration of the structural basis for sequence specificity in A3 substrate selection, and demonstrated the utility of graph-based approaches in evaluating a large number of structural models generated in silico.

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APOBEC3(A3)-DNA structures have been resolved with modified deaminase domains.

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Structural modelling of interaction between wild-type A3 domains and DNA substrates.

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Graph-based representations reveal structural differences across A3-DNA interfaces.

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Using in silico mutagenesis we compared substrate preference of multiple A3 domains.

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Graph-based approaches can efficiently compare a large number of structural models.

Quantifying the thermodynamics of protein unfolding using 2D NMR spectroscopy

A topic that has attracted considerable interest in recent years is the possibility to perform thermodynamic studies of proteins directly in-cell or in complex environments which mimic the cellular interior. Nuclear magnetic resonance (NMR) could be an attractive technique for these studies but its applicability has so far been limited by technical issues. Here, we demonstrate that 2D NMR methods can be successfully applied to measure thermodynamic parameters provided that a suitable choice of the residues used for the calculation is made. We propose a new parameter, named RAD, which reflects the level of protection of a specific amide proton in the protein core and can guide through the selection of the resonances. We also suggest a way to calibrate the volumes to become independent of technical limitations. The methodology we propose leads to stability curves comparable to that calculated from CD data and provides a new tool for thermodynamic measurements in complex environments.

Global phosphoproteomic analysis of Ebola virions reveals a novel role for VP35 phosphorylation-dependent regulation of genome transcription

Ebola virus (EBOV) causes severe human disease with a high case fatality rate. The balance of evidence implies that the virus circulates in bats. The molecular basis for host-viral interactions, including the role for phosphorylation during infections, is largely undescribed. To address this, and to better understand the biology of EBOV, the phosphorylation of EBOV proteins was analyzed in virions purified from infected monkey Vero-E6 cells and bat EpoNi/22.1 cells using high-resolution mass spectrometry. All EBOV structural proteins were detected with high coverage, along with phosphopeptides. Phosphorylation sites were identified in all viral structural proteins. Comparison of EBOV protein phosphorylation in monkey and bat cells showed only partial overlap of phosphorylation sites, with shared sites found in NP, VP35, and VP24 proteins, and no common sites in the other proteins. Three-dimensional structural models were built for NP, VP35, VP40, GP, VP30 and VP24 proteins using available crystal structures or by de novo structure prediction to elucidate the potential role of the phosphorylation sites. Phosphorylation of one of the identified sites in VP35, Thr-210, was demonstrated to govern the transcriptional activity of the EBOV polymerase complex. Thr-210 phosphorylation was also shown to be important for VP35 interaction with NP. This is the first study to compare phosphorylation of all EBOV virion proteins produced in primate versus bat cells, and to demonstrate the role of VP35 phosphorylation in the viral life cycle. The results uncover a novel mechanism of EBOV transcription and identify novel targets for antiviral drug development.

Mining the PDB for Tractable Cases Where X-ray Crystallography Combined with Fragment Screens Can Be Used to Systematically Design Protein-Protein Inhibitors: Two Test Cases Illustrated by IL1β-IL1R and p38α-TAB1 Complexes

Nowadays, it is possible to combine X-ray crystallography and fragment screening in a medium throughput fashion to chemically probe the surfaces used by proteins to interact and use the outcome of the screens to systematically design protein-protein inhibitors. To prove it, we first performed a bioinformatics analysis of the Protein Data Bank protein complexes, which revealed over 400 cases where the crystal lattice of the target in the free form is such that large portions of the interacting surfaces are free from lattice contacts and therefore accessible to fragments during soaks. Among the tractable complexes identified, we then performed single fragment crystal screens on two particular interesting cases: the Il1β-ILR and p38α-TAB1 complexes. The result of the screens showed that fragments tend to bind in clusters, highlighting the small-molecule hotspots on the surface of the target protein. In most of the cases, the hotspots overlapped with the binding sites of the interacting proteins.

Role of substrate-product frustration on enzyme functional dynamics

Natural enzymes often have enormous catalytic power developed by evolution. Revealing the underlying physical strategy used by enzymes to achieve high catalysis efficiency is one of the central focuses in the field of biological physics. Our recent work demonstrated that multisubstrate enzymes can utilize steric frustration encountered in the substrate-product cobound complex to overcome the bottleneck of the enzymatic cycle [W. Li et al., Phys. Rev. Lett. 122, 238102 (2019)10.1103/PhysRevLett.122.238102]. However, the key atomic-level interactions by which the steric frustration contributes to the enzymatic cycle remain elusive. In this work we study the microscopic mechanism for the role of the substrate-product frustration on the key physical steps in the enzymatic cycle of adenylate kinase (AdK), a multisubstrate enzyme catalyzing the reversible phosphoryl transfer reaction ATP+AMP⇋ADP+ADP. By using atomistic molecular dynamics simulations with enhanced sampling, we showed that the competitive interactions from the phosphate groups of the substrate ATP and product ADP in the ATP-ADP cobound complex of the AdK lead to local frustration in the binding pockets. Such local frustration disrupts the hydrogen bond network around the binding pockets, which causes lowered barrier height for the opening of the enzyme conformations and expedited release of the bottleneck product ADP. Our results directly demonstrated from the atomistic level that the local frustration in the active sites of the enzyme can be utilized to facilitate the key physical steps of the enzymatic cycle, providing numerical evidence to the predictions of the previous theoretical work.

A Novel Neutralizing Antibody Targeting a Unique Cross-Reactive Epitope on the hi Loop of Domain II of the Envelope Protein Protects Mice against Duck Tembusu Virus

The Flavivirus E protein induces protective immunity, and its Abs cause serious problems for serodiagnosis because of the difficulty in differentiating cross-reactive Abs. Moreover, cross-reactive Abs may increase disease severity after secondary Flavivirus infections via Ab-dependent enhancement. Cross-reactive epitopes are therefore critical for understanding serodiagnosis and improving the general knowledge of Flavivirus infections. A minimal epitope, 227GSSAGTWQN235, was identified by a neutralizing mAb 1G2 against duck Tembusu virus (DTMUV), which recognized only monomer E protein under nonreducing conditions. It was unexpectedly found that mutations in the epitope residues G231 or W233 completely abolished reactivity to 1G2 and sera from mice infected with Japanese encephalitis virus, West Nile virus, and Zika virus. An immunofluorescence assay confirmed that mAb 1G2 could cross-react with the E proteins from Japanese encephalitis virus, West Nile virus, and Zika virus. Protein and virus modeling revealed that the epitope was surface accessible in the mature virus and located in the hi loop of domain II. The neutralization of DTMUV by 1G2 played a clear therapeutic role in mouse models. The passive transfer of 1G2 resulted in 100% survival, reduced weight loss, and the complete clearance of DTMUV from the blood of BALB/c mice. Our findings document, for the first time to our knowledge, that mAb 1G2 targets the cross-reactive epitope on the hi loop of domain II in the E protein and might be of potential therapeutic value in treating DTMUV infection and improve the understanding of the issues related to serodiagnosis.

Study on Cecropin B2 Production via Construct Bearing Intein Oligopeptide Cleavage Variants

In this study, genetic engineering was applied to the overexpression of the antimicrobial peptide (AMP) cecropin B2 (cecB2). pTWIN1 vector with a chitin-binding domain (CBD) and an auto-cleavage Ssp DnaB intein (INT) was coupled to the cecB2 to form a fusion protein construct and expressed via Escherichia coli ER2566. The cecB2 was obtained via the INT cleavage reaction, which was highly related to its adjacent amino acids. Three oligopeptide cleavage variants (OCVs), i.e., GRA, CRA, and SRA, were used as the inserts located at the C-terminus of the INT to facilitate the cleavage reaction. SRA showed the most efficient performance in accelerating the INT self-cleavage reaction. In addition, in order to treat the INT as a biocatalyst, a first-order rate equation was applied to fit the INT cleavage reaction. A possible inference was proposed for the INT cleavage promotion with varied OCVs using a molecular dynamics (MD) simulation. The production and purification via the CBD-INT-SRA-cecB2 fusion protein resulted in a cecB2 yield of 58.7 mg/L with antimicrobial activity.

Machine Learning for Prioritization of Thermostabilizing Mutations for G-Protein Coupled Receptors

Although the three-dimensional structures of G-protein coupled receptors (GPCRs), the largest superfamily of drug targets, have enabled structure-based drug design, there are no structures available for 87% of GPCRs. This is due to the stiff challenge in purifying the inherently flexible GPCRs. Identifying thermostabilized mutant GPCRs via systematic alanine scanning mutations has been a successful strategy in stabilizing GPCRs, but it remains a daunting task for each GPCR. We developed a computational method that combines sequence-, structure-, and dynamics-based molecular properties of GPCRs that recapitulate GPCR stability, with four different machine learning methods to predict thermostable mutations ahead of experiments. This method has been trained on thermostability data for 1231 mutants, the largest publicly available data set. A blind prediction for thermostable mutations of the complement factor C5a receptor 1 retrieved 36% of the thermostable mutants in the top 50 prioritized mutants compared to 3% in the first 50 attempts using systematic alanine scanning.

Ole e 15 and its human counterpart -PPIA- chimeras reveal an heterogeneous IgE response in olive pollen allergic patients

Olive pollen is a major cause of immunoglobulin E (IgE)-mediated allergy in Mediterranean countries. It is expected to become a worldwide leading allergenic source because olive cultivation is increasing in many countries. Ole e 15 belongs to the cyclophilin pan-allergen family, which includes highly cross-reactive allergens from non-related plant, animal and mold species. Here, the amino acid differences between Ole e 15 and its weak cross-reactive human homolog PPIA were grafted onto Ole e 15 to assess the contribution of specific surface areas to the IgE-binding. Eight Ole e 15-PPIA chimeras were produced in E. coli, purified and tested with 20 sera from Ole e 15-sensitized patients with olive pollen allergy by ELISA experiments. The contribution of linear epitopes was analyzed using twelve overlapping peptides spanning the entire Ole e 15 sequence. All the patients displayed a diverse reduction of the IgE-reactivity to the chimeras, revealing a highly polyclonal and patient-specific response to Ole e 15. IgE-epitopes are distributed across the entire Ole e 15 surface. Two main surface areas containing relevant conformational epitopes have been characterized. This is the first study to identify important IgE-binding regions on the surface of an allergenic cyclophilin.

Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.

Soft regions of protein surface are potent for stable dimer formation

By having knowledge about the characteristics of protein interaction interfaces, we will be able to manipulate protein complexes for therapies. Dimer state is considered as the primary alphabet of the most proteins' quaternary structure. The properties of binding interface between subunits and of noninterface region define the specificity and stability of the intended protein complex. Considering some topological properties and amino acids' affinity for binding in interfaces of protein dimers, we construct the interface-specific recurrence plots. The data obtained from recurrence quantitative analysis, and accessibility-related metrics help us to classify the protein dimers into four distinct classes. Some mechanical properties of binding interfaces are computed for each predefined class of the dimers. The computed mechanical characteristics of binding patch region are compared with those of nonbinding region of proteins. Our observations indicate that the mechanical properties of protein binding sites have a decisive impact on determining the dimer stability. We introduce a new concept in analyzing protein structure by considering mechanical properties of protein structure. We conclude that the interface region between subunits of stable dimers is usually mechanically softer than the interface of unstable protein dimers. AbbreviationsAABaverage affinity for bindingANManisotropic network modelAPCaffinity propagation clusteringASAaccessible surface areaCCDinter residues distanceCSCcomplex stability codeDMdistance matrixΔG_dissPISA-computed dissociation free energyGNMGaussian normal mode analysisNMAnormal mode analysisPBPprotein binding patchPISAproteins, interfaces, structures and assembliesrASArelative accessible area in respect to unfolded state of residuesRMrecurrence matrixrPrelative protrusionRPrecurrence plotRQArecurrence quantitative analysisSEMstandard error of meanCommunicated by Ramaswamy H. Sarma.

CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction

CavityPlus is a web server that offers protein cavity detection and various functional analyses. Using protein three-dimensional structural information as the input, CavityPlus applies CAVITY to detect potential binding sites on the surface of a given protein structure and rank them based on ligandability and druggability scores. These potential binding sites can be further analysed using three submodules, CavPharmer, CorrSite, and CovCys. CavPharmer uses a receptor-based pharmacophore modelling program, Pocket, to automatically extract pharmacophore features within cavities. CorrSite identifies potential allosteric ligand-binding sites based on motion correlation analyses between cavities. CovCys automatically detects druggable cysteine residues, which is especially useful to identify novel binding sites for designing covalent allosteric ligands. Overall, CavityPlus provides an integrated platform for analysing comprehensive properties of protein binding cavities. Such analyses are useful for many aspects of drug design and discovery, including target selection and identification, virtual screening, de novo drug design, and allosteric and covalent-binding drug design. The CavityPlus web server is freely available at http://repharma.pku.edu.cn/cavityplus or http://www.pkumdl.cn/cavityplus.

Structural insights into the combinatorial effects of antimicrobial peptides reveal a role of aromatic-aromatic interactions in antibacterial synergism

The recent development of plants that overexpress antimicrobial peptides (AMPs) provides opportunities for controlling plant diseases. Because plants employ a broad-spectrum antimicrobial defense, including those based on AMPs, transgenic modification for AMP overexpression represents a potential way to utilize a defense system already present in plants. Herein, using an array of techniques and approaches, we report on VG16KRKP and KYE28, two antimicrobial peptides, which in combination exhibit synergistic antimicrobial effects against plant pathogens and are resistant against plant proteases. Investigating the structural origin of these synergistic antimicrobial effects with NMR spectroscopy of the complex formed between these two peptides and their mutated analogs, we demonstrate the formation of an unusual peptide complex, characterized by the formation of a bulky hydrophobic hub, stabilized by aromatic zippers. Using three-dimensional structure analyses of the complex in bacterial outer and inner membrane components and when bound to lipopolysaccharide (LPS) or bacterial membrane mimics, we found that this structure is key for elevating antimicrobial potency of the peptide combination. We conclude that the synergistic antimicrobial effects of VG16KRKP and KYE28 arise from the formation of a well-defined amphiphilic dimer in the presence of LPS and also in the cytoplasmic bacterial membrane environment. Together, these findings highlight a new application of solution NMR spectroscopy to solve complex structures to study peptide-peptide interactions, and they underscore the importance of structural insights for elucidating the antimicrobial effects of AMP mixtures.

Large fatty acid-derived Aβ42 oligomers form ring-like assemblies

As the primary toxic species in the etiology of Alzheimer disease (AD) are low molecular weight oligomers of Aβ, it is crucial to understand the structure of Aβ oligomers for gaining molecular insights into AD pathology. We have earlier demonstrated that in the presence of fatty acids, Aβ42 peptides assemble as 12-24mer oligomers. These Large Fatty Acid-derived Oligomers (LFAOs) exist predominantly as 12mers at low and as 24mers at high concentrations. The 12mers are more neurotoxic than the 24mers and undergo self-replication, while the latter propagate to morphologically distinct fibrils with succinct pathological consequences. In order to glean into their functional differences and similarities, we have determined their structures in greater detail by combining molecular dynamic simulations with biophysical measurements. We conjecture that the LFAO are made of Aβ units in an S-shaped conformation, with the 12mers forming a double-layered hexamer ring (6 × 2) while the structure of 24mers is a double-layered dodecamer ring (12 × 2). A closer inspection of the (6 × 2) and (12 × 2) structures reveals a concentration and pH dependent molecular reorganization in the assembly of 12 to 24mers, which seems to be the underlying mechanism for the observed biophysical and cellular properties of LFAOs.

A viral-fusion-peptide-like molecular switch drives membrane insertion of botulinum neurotoxin A1

Botulinum neurotoxin (BoNT) delivers its protease domain across the vesicle membrane to enter the neuronal cytosol upon vesicle acidification. This process is mediated by its translocation domain (H_N), but the molecular mechanism underlying membrane insertion of H_N remains poorly understood. Here, we report two crystal structures of BoNT/A1 H_N that reveal a novel molecular switch (termed BoNT-switch) in H_N, where buried α-helices transform into surface-exposed hydrophobic β-hairpins triggered by acidic pH. Locking the BoNT-switch by disulfide trapping inhibited the association of H_N with anionic liposomes, blocked channel formation by H_N, and reduced the neurotoxicity of BoNT/A1 by up to ~180-fold. Single particle counting studies showed that an acidic environment tends to promote BoNT/A1 self-association on liposomes, which is partly regulated by the BoNT-switch. These findings suggest that the BoNT-switch flips out upon exposure to the acidic endosomal pH, which enables membrane insertion of H_N that subsequently leads to LC delivery.

The translocation domain (H_N) of Botulinum neurotoxins (BoNTs) mediates the delivery of the BoNT light chain (LC) into neuronal cytosol. Here the authors provide insights into H_N membrane insertion by determining the crystal structure of BoNT/A1 H_N at acidic pH, which reveals a molecular switch in H_N, where buried α-helices are transformed into surface-exposed hydrophobic β-hairpins.

Vasoinhibin comprises a three-helix bundle and its antiangiogenic domain is located within the first 79 residues

Vasoinhibin belongs to a family of angiogenesis inhibitors generated when the fourth α-helix (H4) of the hormone prolactin (PRL) is removed by specific proteolytic cleavage. The antiangiogenic properties are absent in uncleaved PRL, indicating that conformational changes create a new bioactive domain. However, the solution structure of vasoinhibin and the location of its bioactive domain are unknown. Molecular dynamic simulation (MD) showed that the loss of H4 exposes the hydrophobic nucleus of PRL and leads to the compression of the molecule into a three-helix bundle that buries the hydrophobic nucleus again. Compression occurs by the movement of loop 1 (L1) and its interaction with α-helix 1 (H1) generating a new L1 conformation with electrostatic and hydrophobic surfaces distinct from those of PRL, that may correspond to a bioactive domain. Consistent with this model, a recombinant protein containing the first 79 amino acids comprising H1 and L1 of human PRL inhibited the proliferation and migration of endothelial cells and upregulated the vasoinhibin target genes, IL1A and ICAM1. This bioactivity was comparable to that of a conventional vasoinhibin having the 123 residues encompassing H1, L1, Η2, L2, and Η3 of human PRL. These findings extend the vasoinhibin family to smaller proteins and provide important structural information, which will aid in antiangiogenic drug development.

Meta-Analysis for Correlating Structure of Bioactive Peptides in Foods of Animal Origin with Regard to Effect and Stability

Amino acid (AA) sequences of 807 bioactive peptides from foods of animal origin were examined in order to correlate peptide structure with activity (antihypertensive, antioxidative, immunomodulatory, antimicrobial, hypolipidemic, antithrombotic, and opioid) and stability in vivo. Food sources, such as milk, meat, eggs, and marine products, show different frequencies of bioactive peptides exhibiting specific effects. There is a correlation of peptide structure and effect, depending on type and position of AA. Opioid peptides contain a high percentage of aromatic AA residues, while antimicrobial peptides show an excess of positively charged AAs. AA residue position is significant, with those in the first and penultimate positions having the biggest effects on peptide activity. Peptides that have activity in vivo contain a high percentage (67%) of proline residues, but the positions of proline in the sequence depend on the length of the peptide. We also discuss the influence of processing on activity of these peptides, as well as methods for predicting release from the source protein and activity of peptides.

Epitope resurfacing on dengue virus-like particle vaccine preparation to induce broad neutralizing antibody

Dengue fever is caused by four different serotypes of dengue virus (DENV) which is the leading cause of worldwide arboviral diseases in humans. Virus-like particles (VLPs) containing flavivirus prM/E proteins have been demonstrated to be a potential vaccine candidate; however, the structure of dengue VLP is poorly understood. Herein VLP derived from DENV serotype-2 were engineered becoming highly matured (mD2VLP) and showed variable size distribution with diameter of ~31 nm forming the major population under cryo-electron microscopy examination. Furthermore, mD2VLP particles of 31 nm diameter possess a T = 1 icosahedral symmetry with a groove located within the E-protein dimers near the 2-fold vertices that exposed highly overlapping, cryptic neutralizing epitopes. Mice vaccinated with mD2VLP generated higher cross-reactive (CR) neutralization antibodies (NtAbs) and were fully protected against all 4 serotypes of DENV. Our results highlight the potential of ‘epitope-resurfaced’ mature-form D2VLPs in inducing quaternary structure-recognizing broad CR NtAbs to guide future dengue vaccine design.

Phosphorylated VP30 of Marburg Virus Is a Repressor of Transcription

The filoviruses Marburg virus (MARV) and Ebola virus (EBOV) cause hemorrhagic fever in humans and nonhuman primates, with high case fatality rates. MARV VP30 is known to be phosphorylated and to interact with nucleoprotein (NP), but its role in regulation of viral transcription is disputed. Here, we analyzed phosphorylation of VP30 by mass spectrometry, which resulted in identification of multiple phosphorylated amino acids. Modeling the full-length three-dimensional structure of VP30 and mapping the identified phosphorylation sites showed that all sites lie in disordered regions, mostly in the N-terminal domain of the protein. Minigenome analysis of the identified phosphorylation sites demonstrated that phosphorylation of a cluster of amino acids at positions 46 through 53 inhibits transcription. To test the effect of VP30 phosphorylation on its interaction with other MARV proteins, coimmunoprecipitation analyses were performed. They demonstrated the involvement of VP30 phosphorylation in interaction with two other proteins of the MARV ribonucleoprotein complex, NP and VP35. To identify the role of protein phosphatase 1 (PP1) in the identified effects, a small molecule, 1E7-03, targeting a noncatalytic site of the enzyme that previously was shown to increase EBOV VP30 phosphorylation was used. Treatment of cells with 1E7-03 increased phosphorylation of VP30 at a cluster of phosphorylated amino acids from Ser-46 to Thr-53, reduced transcription of MARV minigenome, enhanced binding to NP and VP35, and dramatically reduced replication of infectious MARV particles. Thus, MARV VP30 phosphorylation can be targeted for development of future antivirals such as PP1-targeting compounds. IMPORTANCE The largest outbreak of MARV occurred in Angola in 2004 to 2005 and had a 90% case fatality rate. There are no approved treatments available for MARV. Development of antivirals as therapeutics requires a fundamental understanding of the viral life cycle. Because of the close similarity of MARV to another member of Filoviridae family, EBOV, it was assumed that the two viruses have similar mechanisms of regulation of transcription and replication. Here, characterization of the role of VP30 and its phosphorylation sites in transcription of the MARV genome demonstrated differences from those of EBOV. The identified phosphorylation sites appeared to inhibit transcription and appeared to be involved in interaction with both NP and VP35 ribonucleoproteins. A small molecule targeting PP1 inhibited transcription of the MARV genome, effectively suppressing replication of the viral particles. These data demonstrate the possibility developing antivirals based on compounds targeting PP1.