Improved prediction of protein side-chain conformations with SCWRL4

Proteomic Tools for the Analysis of Cytoskeleton Proteins

Proteomic analyses have become an essential part of the toolkit of the molecular biologist, given the widespread availability of genomic data and open source or freely accessible bioinformatics software. Tools are available for detecting homologous sequences, recognizing functional domains, and modeling the three-dimensional structure for any given protein sequence, as well as for predicting interactions with other proteins or macromolecules. Although a wealth of structural and functional information is available for many cytoskeletal proteins, with representatives spanning all of the major subfamilies, the majority of cytoskeletal proteins remain partially or totally uncharacterized. Moreover, bioinformatics tools provide a means for studying the effects of synthetic mutations or naturally occurring variants of these cytoskeletal proteins. This chapter discusses various freely available proteomic analysis tools, with a focus on in silico prediction of protein structure and function. The selected tools are notable for providing an easily accessible interface for the novice while retaining advanced functionality for more experienced computational biologists.

Breaking bad: Better call gingerol for improving antibiotic susceptibility of Pseudomonas aeruginosa by inhibiting multiple quorum sensing pathways

Pseudomonas aeruginosa is recognized as a bacterium with many bullets in its armoury and the Achilles heel of the bacterium is that it exudes several pathways that lead to pathogenicity thereby making the application of the strain cautious since the bacterium is known as a 'superbug' ergo, being resistant to multiple antibiotics. The mechanisms of pathogenicity are mainly driven by quorum sensing (QS), a phenomenon that works on cell-cell communication through classical ligand-receptor interactions. QS-mediated pathways enable control of this organism impossible even with the use of antibiotics. Henceforth, interfering with the QS pathways serves as a new mode of action for futuristic antibiotics to decrease the distress of this microbe. We propose gingerol to interfere with various QS-receptors of P. aeruginosa (LasR, PhzR and RhlR) which were deduced using in silico approach and validated in vitro by assessing its impact on EPS, biofilm, pyocyanin and rhamnolipid of the microbe. Further, gingerol was found to increase the antibacterial potency of the antibiotic when applied in integration with ciprofloxacin. The findings provide an insight about preferring the integrated approach of using QS-inhibitors (QSI) in tandem with antibiotics for holistic strategy in fight against the phenomenon of antibiotic resistance acquired by microbes.

Structural determinants and regulation of spontaneous activity in GABAA receptors

GABAA receptors are vital for controlling neuronal excitability and can display significant levels of constitutive activity that contributes to tonic inhibition. However, the mechanisms underlying spontaneity are poorly understood. Here we demonstrate a strict requirement for β3 subunit incorporation into receptors for spontaneous gating, facilitated by α4, α6 and δ subunits. The crucial molecular determinant involves four amino acids (GKER) in the β3 subunit's extracellular domain, which interacts with adjacent receptor subunits to promote transition to activated, open channel conformations. Spontaneous activity is further regulated by β3 subunit phosphorylation and by allosteric modulators including neurosteroids and benzodiazepines. Promoting spontaneous activity reduced neuronal excitability, indicating that spontaneous currents will alter neural network activity. This study demonstrates how regional diversity in GABAA receptor isoform, protein kinase activity, and neurosteroid levels, can impact on tonic inhibition through the modulation of spontaneous GABAA receptor gating.

Elucidating the molecular mechanisms associated with TARS2-related mitochondrial disease

TARS2 encodes human mitochondrial threonyl tRNA-synthetase that is responsible for generating mitochondrial Thr-tRNAThr and clearing mischarged Ser-tRNAThr during mitochondrial translation. Pathogenic variants in TARS2 have hitherto been reported in a pair of siblings and an unrelated patient with an early onset mitochondrial encephalomyopathy and a combined respiratory chain enzyme deficiency in muscle. We here report five additional unrelated patients with TARS2-related mitochondrial diseases, expanding the clinical phenotype to also include epilepsy, dystonia, hyperhidrosis and severe hearing impairment. Additionally, we document seven novel TARS2 variants-one nonsense variant and six missense variants-that we demonstrate are pathogenic and causal of the disease presentation based on population frequency, homology modelling and functional studies that show the effects of the pathogenic variants on TARS2 stability and/or function.

Computational design and experimental substantiation of conformationally constrained peptides from the complex interfaces of transcriptional enhanced associate domains with their cofactors in gastric cancer

Transcriptional enhanced associate domains (Teads) are the downstream effectors of the hippo signaling pathway and have been recognized as attractive druggable targets of gastric cancer. The biological function of Teads is regulated by diverse cofactors. In this study, the intermolecular interactions of Teads with their cognate cofactors were systematically characterized at structural, thermodynamic and dynamic levels. The Teads possess a double-stranded helical hairpin that is surrounded by three independent structural elements β-sheet, α-helix and Ω-loop of cofactor proteins and plays a central role in recognition and association with cofactors. A number of functional peptides were split from the hairpin region at Tead-cofactor complex interfaces, which, however, cannot maintain in native conformation without the support of protein context and would therefore incur a considerable entropy penalty upon competitively rebinding to the interfaces. Here, we further used disulfide and hydrocarbon bridges to cyclize and staple the hairpin and helical peptides, respectively. The chemical modification strategies were demonstrated to effectively constrain peptide conformation into active state and to largely reduce peptide flexibility in free state, thus considerably improving their affinity. Since the cyclization and stapling only minimize the indirect entropy cost but do not influence the direct enthalpy effect upon peptide binding, the designed conformationally constrained peptides can retain in their native selectivity over different cofactors. This is particularly interesting because it means that the cyclized/stapled, affinity-improved peptides can specifically compete with their parent Teads for the cofactor arrays as they share consistent target specificity.

The role of the half-turn in determining structures of Alzheimer's Aβ wild-type and mutants

Half-turns are shown to be the main determinants of many experimental Alzheimer's Aβ fibril structures. Fibril structures contain three half-turn types, βα_Rβ, βα_Lβ and βεβ which each result in a ∼90° bend in a β-strand. It is shown that only these half-turns enable cross-β stacking and thus the right-angle fold seen in fibrils is an intrinsic feature of cross-β. Encoding a strand as a conformational sequence in β, α_R, α_L and ε(β_L), pairwise combination rules for consecutive half-turns are used to decode this sequence to give the backbone path. This reveals how structures would be dramatically affected by a deletion. Using a wild-type Aβ(42) fibril structure and the pairwise combination rules, the Osaka deletion is predicted to result in exposure of surfaces that are mutually shielding from the solvent. Molecular dynamics simulations on an 11-mer β-sheet of Alzheimer's Aβ(40) of the Dutch (E22Q), Iowa (D23N), Arctic (E22G), and Osaka (E22Δ) mutants, show the crucial role glycine plays in the positioning of βα_Rβ half-turns. Their "in-phase" positions along the sequence in the wild-type, Dutch mutant and Iowa mutant means that the half-folds all fold to the same side creating the same closed structure. Their out-of-phase positions in Arctic and Osaka mutants creates a flatter structure in the former and an S-shape structure in the latter which, as predicted, exposes surfaces on the inside in the closed wild-type to the outside. This is consistent with the gain of interaction model and indicates how domain swapping might explain the Osaka mutant's unique properties.

Intrinsic relative preference profile of pan-kinase inhibitor drug staurosporine towards the clinically occurring gatekeeper mutations in Protein Tyrosine Kinases

Protein tyrosine kinases (PTKs) have been recognized as the attractive druggable targets of various diseases including cancer. However, many PTKs are clinically observed to establish a gatekeeper mutation in the peripheral hinge section of active site, which plays a primary role in development of acquired drug resistance to kinase inhibitors. The natural product Staurosporine, an ATP-competitive reversible pan-kinase inhibitor, has been found to exhibit wild type-sparing selectivity for some PTK gatekeeper mutants. In this study, totally 23 acquired drug-resistant gatekeeper mutations harbored on 17 PTKs involved in diverse cancers were curated, from which only five amino acid types, namely Thr, Met, Val, Leu and Ile, were observed at both wild-type and mutant residues of these clinically occurring gatekeeper sites. Here, an integrative strategy that combined molecular modeling and kinase assay was described to systematically investigate the relative preference of Staurosporine towards the five gatekeeper amino acid types in real kinase context and in a psendokinase model. A kinase-free, intrinsic relative preference profile of Staurosporine to gatekeeper amino acids was created: (dispreferred) Thr⊳Val⊳Ile⊳Leu⊳Met (preferred). It is found that kinase context has no essential effect on the profile; different kinases and even psendokinase can obtain a consistent conclusion for the preference order. Theoretically, we can use the profile to predict Staurosporine response to any gatekeeper mutation between the five amino acid types in any PTK. Structural and energetic analyses revealed that the multiple-aromatic ring system of Staurosporine can form multiple noncovalent interactions with the weakly polar side chain of Met and can pack tightly or moderately against the nonpolar side chains of Val, Ile and Leu, thus stabilizing the kinase-inhibitor system (ΔU < 0), whereas the polar side chain of Thr may cause unfavorable electronegative and solvent effects with the aromatic electrons of Staurosporine, thus destabilizing the system (ΔU > 0).

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI.

Estimating the binding affinities of protein-protein interactions (PPIs) is crucial to understand protein function and design new functional proteins. Since the experimental measurement in wet-labs is labor-intensive and time-consuming, fast and accurate in silico approaches have received much attention. Although considerable efforts have been made in this direction, predicting the effects of mutations on the protein-protein binding affinity is still a challenging research problem. In this work, we introduce GeoPPI, a novel computational approach that uses deep geometric representations of protein complexes to predict the effects of mutations on the binding affinity. The geometric representations are first learned via a self-supervised learning scheme and then integrated with gradient-boosting trees to accomplish the prediction. We find that the learned representations encode meaningful patterns underlying the interactions between atoms in protein structures. Also, extensive tests on major benchmark datasets show that GeoPPI has made an important improvement over the existing methods in predicting the effects of mutations on the binding affinity.

A novel de novo HDAC8 missense mutation causing Cornelia de Lange syndrome

Background

Cornelia de Lange syndrome (CdLS) is a rare and clinically variable syndrome characterized by growth impairment, multi‐organ anomalies, and a typical set of facial dysmorphisms. Here we describe a 2‐year‐old female child harboring a novel de novo missense variant in HDAC8, whose phenotypical score, according to the recent consensus on CdLS clinical diagnostic criteria, allowed the diagnosis of a non‐classic CdLS.

Methods

Clinical exome sequencing was performed on the trio, identifying a de novo heterozygous variant in HDAC8 (NM_018486; c. 356C>G p.Thr119Arg). Molecular modeling was performed to evaluate putative functional consequence of the HDAC8 protein.

Results

The variant HDAC8 c.356C>G is classified as pathogenic following the ACMG (American College of Medical Genetics and Genomics)/AMP (Association for Molecular Pathology) guidelines. By molecular modeling, we confirmed the deleterious effect of this variant, since the amino acid change compromises the conformational flexibility of the HDAC8 loop required for optimal catalytic function.

Conclusion

We described a novel Thr119Arg mutation in HDAC8 in a patient displaying the major phenotypic traits of the CdLS. Our results suggest that a modest change outside an active site is capable of triggering global structural changes that propagate through the protein scaffold to the catalytic site, creating de facto haploinsufficiency.

Computational Analysis of the Crystal and Cryo-EM Structures of P-Loop Channels with Drugs

The superfamily of P-loop channels includes various potassium channels, voltage-gated sodium and calcium channels, transient receptor potential channels, and ionotropic glutamate receptors. Despite huge structural and functional diversity of the channels, their pore-forming domain has a conserved folding. In the past two decades, scores of atomic-scale structures of P-loop channels with medically important drugs in the inner pore have been published. High structural diversity of these complexes complicates the comparative analysis of these structures. Here we 3D-aligned structures of drug-bound P-loop channels, compared their geometric characteristics, and analyzed the energetics of ligand-channel interactions. In the superimposed structures drugs occupy most of the sterically available space in the inner pore and subunit/repeat interfaces. Cationic groups of some drugs occupy vacant binding sites of permeant ions in the inner pore and selectivity-filter region. Various electroneutral drugs, lipids, and detergent molecules are seen in the interfaces between subunits/repeats. In many structures the drugs strongly interact with lipid and detergent molecules, but physiological relevance of such interactions is unclear. Some eukaryotic sodium and calcium channels have state-dependent or drug-induced π-bulges in the inner helices, which would be difficult to predict. The drug-induced π-bulges may represent a novel mechanism of gating modulation.

A Brief History of De Novo Protein Design: Minimal, Rational, and Computational

Protein design has come of age, but how will it mature? In the 1980s and the 1990s, the primary motivation for de novo protein design was to test our understanding of the informational aspect of the protein-folding problem; i.e., how does protein sequence determine protein structure and function? This necessitated minimal and rational design approaches whereby the placement of each residue in a design was reasoned using chemical principles and/or biochemical knowledge. At that time, though with some notable exceptions, the use of computers to aid design was not widespread. Over the past two decades, the tables have turned and computational protein design is firmly established. Here, I illustrate this progress through a timeline of de novo protein structures that have been solved to atomic resolution and deposited in the Protein Data Bank. From this, it is clear that the impact of rational and computational design has been considerable: More-complex and more-sophisticated designs are being targeted with many being resolved to atomic resolution. Furthermore, our ability to generate and manipulate synthetic proteins has advanced to a point where they are providing realistic alternatives to natural protein functions for applications both in vitro and in cells. Also, and increasingly, computational protein design is becoming accessible to non-specialists. This all begs the questions: Is there still a place for minimal and rational design approaches? And, what challenges lie ahead for the burgeoning field of de novo protein design as a whole?

Phosphate binding sites prediction in phosphorylation-dependent protein-protein interactions

MOTIVATION

Phosphate binding plays an important role in modulating protein-protein interactions, which are ubiquitous in various biological processes. Accurate prediction of phosphate binding sites is an important but challenging task. Small size and diversity of phosphate binding sites lead to a substantial challenge for developing accurate prediction methods.

RESULTS

Here we present the phosphate binding site predictor (PBSP), a novel and accurate approach to identifying phosphate binding sites from protein structures. PBSP combines an energy-based ligand-binding sites identification method with reverse focused docking using a phosphate probe. We show that PBSP outperforms not only general ligand binding sites predictors but also other existing phospholigand-specific binding sites predictors. It achieves ∼95% success rate for top 10 predicted sites with an average Matthews correlation coefficient (MCC) value of 0.84 for successful predictions. PBSP can accurately predict phosphate binding modes, with average position error of 1.4 Å and 2.4 Å in bound and unbound datasets, respectively. Lastly, visual inspection of the predictions is conducted. Reasons for failed predictions are further analyzed and possible ways to improve the performance are provided. These results demonstrate a novel and accurate approach to phosphate binding sites identification in protein structures.

AVAILABILITY

The software and benchmark datasets are freely available at http://web.pkusz.edu.cn/wu/PBSP/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

UNRES-Dock-protein-protein and peptide-protein docking by coarse-grained replica-exchange MD simulations

MOTIVATION

The majority of the proteins in living organisms occur as homo- or hetero-multimeric structures. Although there are many tools to predict the structures of single-chain proteins or protein complexes with small ligands, peptide-protein and protein-protein docking is more challenging. In this work, we utilized multiplexed replica-exchange molecular dynamics (MREMD) simulations with the physics-based heavily coarse-grained UNRES model, which provides more than a 1000-fold simulation speed-up compared with all-atom approaches to predict structures of protein complexes.

RESULTS

We present a new protein-protein and peptide-protein docking functionality of the UNRES package, which includes a variable degree of conformational flexibility. UNRES-Dock protocol was tested on a set of 55 complexes with size from 43 to 587 amino-acid residues, showing that structures of the complexes can be predicted with good quality, if the sampling of the conformational space is sufficient, especially for flexible peptide-protein systems. The developed automatized protocol has been implemented in the standalone UNRES package and in the UNRES server.

AVAILABILITY AND IMPLEMENTATION

UNRES server: http://unres-server.chem.ug.edu.pl; UNRES package and data used in testing of UNRES-Dock: http://unres.pl.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Structural and evolutionary exploration of the IL-3 family and its alpha subunit receptors

Interleukin-3 (IL-3) is a cytokine belonging to the family of common β (βc) and is involved in various biological systems. Its activity is mediated by the interaction with its receptor (IL-3R), a heterodimer composed of two distinct subunits: IL-3Rα and βc. IL-3 and its receptor, especially IL-3Rα, play a crucial role in pathologies like inflammatory diseases and therefore are interesting therapeutic targets. Here, we have performed an analysis of these proteins and their interaction based on structural and evolutionary information. We highlighted that IL-3 and IL-3Rα structural architectures are conserved across evolution and shared with other proteins belonging to the same βc family interleukin-5 (IL-5) and granulocyte-macrophage colony-stimulating factor (GM-CSF). The IL-3Rα/IL-3 interaction is mediated by a large interface in which most residues are surprisingly not conserved during evolution and across family members. In spite of this high variability, we suggested small regions constituted by few residues conserved during the evolution in both proteins that could be important for the binding affinity.

Molecular docking-aided identification of small molecule inhibitors targeting β-catenin-TCF4 interaction

Here we report a molecular docking-based approach to identify small molecules that can target the β-catenin (β-cat)-TCF4 protein-protein interaction (PPI), a key effector complex for nuclear Wnt signaling activity. Specifically, we developed and optimized a computational model of β-cat using publicly available β-cat protein crystal structures, and existing β-cat-TCF4 interaction inhibitors as the training set. Using our computational model to an in silico screen predicted 27 compounds as good binders to β-cat, of which 3 were identified to be effective against a Wnt-responsive luciferase reporter. In vitro functional validation experiments revealed GB1874 as an inhibitor of the Wnt pathway that targets the β-cat-TCF4 PPI. GB1874 also affected the proliferation and stemness of Wnt-addicted colorectal cancer (CRC) cells in vitro. Encouragingly, GB1874 inhibited the growth of CRC tumor xenografts in vivo, thus demonstrating its potential for further development into therapeutics against Wnt-associated cancer indications.

Landscape-Based Mutational Sensitivity Cartography and Network Community Analysis of the SARS-CoV-2 Spike Protein Structures: Quantifying Functional Effects of the Circulating D614G Variant

We developed and applied a computational approach to simulate functional effects of the global circulating mutation D614G of the SARS-CoV-2 spike protein. All-atom molecular dynamics simulations are combined with deep mutational scanning and analysis of the residue interaction networks to investigate conformational landscapes and energetics of the SARS-CoV-2 spike proteins in different functional states of the D614G mutant. The results of conformational dynamics and analysis of collective motions demonstrated that the D614 site plays a key regulatory role in governing functional transitions between open and closed states. Using mutational scanning and sensitivity analysis of protein residues, we identified the stability hotspots in the SARS-CoV-2 spike structures of the mutant trimers. The results suggest that the D614G mutation can induce the increased stability of the open form acting as a driver of conformational changes, which may result in the increased exposure to the host receptor and promote infectivity of the virus. The network community analysis of the SARS-CoV-2 spike proteins showed that the D614G mutation can enhance long-range couplings between domains and strengthen the interdomain interactions in the open form, supporting the reduced shedding mechanism. This study provides the landscape-based perspective and atomistic view of the allosteric interactions and stability hotspots in the SARS-CoV-2 spike proteins, offering a useful insight into the molecular mechanisms underpinning functional effects of the global circulating mutations.

Dynamic Profiling of Binding and Allosteric Propensities of the SARS-CoV-2 Spike Protein with Different Classes of Antibodies: Mutational and Perturbation-Based Scanning Reveals the Allosteric Duality of Functionally Adaptable Hotspots

The functional adaptability and conformational plasticity of SARS-CoV-2 spike proteins allow for the efficient modulation of complex phenotypic responses to the host receptor and antibodies. In this study, we combined atomistic simulations with mutational and perturbation-based scanning approaches to examine binding mechanisms of the SARS-CoV-2 spike proteins with three different classes of antibodies. The ensemble-based profiling of binding and allosteric propensities of the SARS-CoV-2 spike protein residues showed that these proteins can work as functionally adaptable and allosterically regulated machines. Conformational dynamics analysis revealed that binding-induced modulation of soft modes can elicit the unique protein response to different classes of antibodies. Mutational scanning heatmaps and sensitivity analysis revealed the binding energy hotspots for different classes of antibodies that are consistent with the experimental deep mutagenesis, showing that differences in the binding affinity caused by global circulating variants in spike positions K417, E484, and N501 are relatively moderate and may not fully account for the observed antibody resistance effects. Through functional dynamics analysis and perturbation-response scanning of the SARS-CoV-2 spike protein residues in the unbound form and antibody-bound forms, we examine how antibody binding can modulate allosteric propensities of spike protein residues and determine allosteric hotspots that control signal transmission and global conformational changes. These results show that residues K417, E484, and N501 targeted by circulating mutations correspond to a group of versatile allosteric centers in which small perturbations can modulate collective motions, alter the global allosteric response, and elicit binding resistance. We suggest that the SARS-CoV-2 S protein may exploit the plasticity of specific allosteric hotspots to generate escape mutants that alter the response to antibody binding without compromising the activity of the spike protein.

Binding of single-mutant epidermal growth factor (EGF) ligands alters the stability of the EGF receptor dimer and promotes growth signaling

The epidermal growth factor receptor (EGFR) is a membrane-anchored tyrosine kinase that is able to selectively respond to multiple extracellular stimuli. Previous studies have indicated that the modularity of this system may be caused by ligand-induced differences in the stability of the receptor dimer. However, this hypothesis has not been explored using single-mutant ligands thus far. Herein, we developed a new approach to identify residues responsible for functional divergence by selecting residues in the epidermal growth factor (EGF) ligand that are conserved among orthologs yet divergent between paralogs. Then, we mutated these residues and assessed the mutants' effects on the receptor using a combination of molecular dynamics (MD) and biochemical techniques. Although the EGF mutants had binding affinities for the EGFR comparable with the WT ligand, the EGF mutants showed differential patterns of receptor phosphorylation and cell growth in multiple cell lines. The MD simulations of the EGF mutants indicated that mutations had long-range effects on the receptor dimer interface. This study shows for the first time that a single mutation in the EGF is sufficient to alter the activation of the EGFR signaling pathway at the cellular level. These results also support that biased ligand-receptor signaling in the tyrosine kinase receptor system can lead to differential downstream outcomes and demonstrate a promising new method to study ligand-receptor interactions.

3D Interaction Homology: Hydropathic Analyses of the "π-Cation" and "π-π" Interaction Motifs in Phenylalanine, Tyrosine, and Tryptophan Residues

Three-dimensional (3D) maps of the hydropathic environments of protein amino acid residues are information-rich descriptors of preferred conformations, interaction types and energetics, and solvent accessibility. The interactions made by each residue are the primary factor for rotamer selection and the secondary, tertiary, and even quaternary protein structure. Our evolving basis set of environmental data for each residue type can be used to understand the protein structure. This work focuses on the aromatic residues phenylalanine, tyrosine, and tryptophan and their structural roles. We calculated and analyzed side chain-to-environment 3D maps for over 70,000 residues of these three types that reveal, with respect to hydrophobic and polar interactions, the environment around each. After binning with backbone ϕ/ψ and side chain χ₁, we clustered each bin by 3D similarities between map-map pairs. For each of the three residue types, four bins were examined in detail: one in the β-pleat, two in the right-hand α-helix, and one in the left-hand α-helix regions of the Ramachandran plot. For high degrees of side chain burial, encapsulation of the side chain by hydrophobic interactions is ubiquitous. The more solvent-exposed side chains are more likely to be involved in polar interactions with their environments. Evidence for π-π interactions was observed in about half of the residues surveyed [phenylalanine (PHE): 53.3%, tyrosine (TYR): 34.1%, and tryptophan (TRP): 55.7%], but on an energy basis, this contributed to only ∼4% of the total. Evidence for π-cation interactions was observed in 14.1% of PHE, 8.3% of TYR, and 26.8% of TRP residues, but on an energy basis, this contributed to only ∼1%. This recognition of even these subtle interactions in the 3D hydropathic environment maps is key support for our interaction homology paradigm of protein structure elucidation and possibly prediction.

Targeting the membrane fusion event of human respiratory syncytial virus with rationally designed α-helical hairpin traps

AIMS

Rational design of protein scaffolds with specific biological functions/activities has attracted much attention over the past decades. In the present study, we systematically examine the trimer-of-hairpins (TOH) motif of human respiratory syncytial virus (RSV) F protein, which plays a central role in viral membrane fusion and is a coiled-coil six-helix bundle formed by the antiparallel intermolecular interaction between three N-terminal heptad-repeat (HRN) helices and three C-terminal heptad-repeat (HRC) helices.

MAIN METHODS

A rational strategy that integrates dynamics simulation, thermodynamics calculation, fluorescence polarization and circular dichroism is proposed to design HRC-targeted α-helical hairpin traps based on the crystal template of HRN core.

KEY FINDINGS

The designed hairpin traps possess a typical helix-turn-helix scaffold that can be stabilized by stapling a disulfide bridge across its helical arms, which are highly structured (helicity >60%) and can mimic the native spatial arrangement of HRN helices in TOH motif to trap the hotspot sites of HRC with effective affinity (K_d is up to 6.4 μM).

SIGNIFICANCE

The designed α-helical hairpin traps can be used as lead entities for further developing TOH-disrupting agents to target RSV membrane fusion event and the proposed rational design strategy can be readily modified to apply for other type I viruses.

Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction

The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug-target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.

Plausible blockers of Spike RBD in SARS-CoV2-molecular design and underlying interaction dynamics from high-level structural descriptors

Abstract

COVID-19 is characterized by an unprecedented abrupt increase in the viral transmission rate (SARS-CoV-2) relative to its pandemic evolutionary ancestor, SARS-CoV (2003). The complex molecular cascade of events related to the viral pathogenicity is triggered by the Spike protein upon interacting with the ACE2 receptor on human lung cells through its receptor binding domain (RBD_Spike). One potential therapeutic strategy to combat COVID-19 could thus be limiting the infection by blocking this key interaction. In this current study, we adopt a protein design approach to predict and propose non-virulent structural mimics of the RBD_Spike which can potentially serve as its competitive inhibitors in binding to ACE2. The RBD_Spike is an independently foldable protein domain, resilient to conformational changes upon mutations and therefore an attractive target for strategic re-design. Interestingly, in spite of displaying an optimal shape fit between their interacting surfaces (attributed to a consequently high mutual affinity), the RBD_Spike–ACE2 interaction appears to have a quasi-stable character due to a poor electrostatic match at their interface. Structural analyses of homologous protein complexes reveal that the ACE2 binding site of RBD_Spike has an unusually high degree of solvent-exposed hydrophobic residues, attributed to key evolutionary changes, making it inherently “reaction-prone.” The designed mimics aimed to block the viral entry by occupying the available binding sites on ACE2, are tested to have signatures of stable high-affinity binding with ACE2 (cross-validated by appropriate free energy estimates), overriding the native quasi-stable feature. The results show the apt of directly adapting natural examples in rational protein design, wherein, homology-based threading coupled with strategic “hydrophobic ↔ polar” mutations serve as a potential breakthrough.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s00894-021-04779-0.

Comparative Perturbation-Based Modeling of the SARS-CoV-2 Spike Protein Binding with Host Receptor and Neutralizing Antibodies: Structurally Adaptable Allosteric Communication Hotspots Define Spike Sites Targeted by Global Circulating Mutations

In this study, we used an integrative computational approach to examine molecular mechanisms and determine functional signatures underlying the role of functional residues in the SARS-CoV-2 spike protein that are targeted by novel mutational variants and antibody-escaping mutations. Atomistic simulations and functional dynamics analysis are combined with alanine scanning and mutational sensitivity profiling of the SARS-CoV-2 spike protein complexes with the ACE2 host receptor and the REGN-COV2 antibody cocktail(REG10987+REG10933). Using alanine scanning and mutational sensitivity analysis, we have shown that K417, E484, and N501 residues correspond to key interacting centers with a significant degree of structural and energetic plasticity that allow mutants in these positions to afford the improved binding affinity with ACE2. Through perturbation-based network modeling and community analysis of the SARS-CoV-2 spike protein complexes with ACE2, we demonstrate that E406, N439, K417, and N501 residues serve as effector centers of allosteric interactions and anchor major intermolecular communities that mediate long-range communication in the complexes. The results provide support to a model according to which mutational variants and antibody-escaping mutations constrained by the requirements for host receptor binding and preservation of stability may preferentially select structurally plastic and energetically adaptable allosteric centers to differentially modulate collective motions and allosteric interactions in the complexes with the ACE2 enzyme and REGN-COV2 antibody combination. This study suggests that the SARS-CoV-2 spike protein may function as a versatile and functionally adaptable allosteric machine that exploits the plasticity of allosteric regulatory centers to fine-tune response to antibody binding without compromising the activity of the spike protein.

Integrated Biophysical Modeling of the SARS-CoV-2 Spike Protein Binding and Allosteric Interactions with Antibodies

Structural and biochemical studies of the severe acute respiratory syndrome (SARS)-CoV-2 spike glycoproteins and complexes with highly potent antibodies have revealed multiple conformation-dependent epitopes highlighting conformational plasticity of spike proteins and capacity for eliciting specific binding and broad neutralization responses. In this study, we used coevolutionary analysis, molecular simulations, and perturbation-based hierarchical network modeling of the SARS-CoV-2 spike protein complexes with a panel of antibodies targeting distinct epitopes to explore molecular mechanisms underlying binding-induced modulation of dynamics and allosteric signaling in the spike proteins. Through coevolutionary analysis of the SARS-CoV-2 spike proteins, we identified highly coevolving hotspots and functional clusters that enable a functional cross-talk between distant allosteric regions in the SARS-CoV-2 spike complexes with antibodies. Coarse-grained and all-atom molecular dynamics simulations combined with mutational sensitivity mapping and perturbation-based profiling of the SARS-CoV-2 receptor-binding domain (RBD) complexes with CR3022 and CB6 antibodies enabled a detailed validation of the proposed approach and an extensive quantitative comparison with the experimental structural and deep mutagenesis scanning data. By combining in silico mutational scanning, perturbation-based modeling, and network analysis of the SARS-CoV-2 spike trimer complexes with H014, S309, S2M11, and S2E12 antibodies, we demonstrated that antibodies can incur specific and functionally relevant changes by modulating allosteric propensities and collective dynamics of the SARS-CoV-2 spike proteins. The results provide a novel insight into regulatory mechanisms of SARS-CoV-2 S proteins showing that antibody-escaping mutations can preferentially target structurally adaptable energy hotspots and allosteric effector centers that control functional movements and allosteric communication in the complexes.

Computational optimization of angiotensin-converting enzyme 2 for SARS-CoV-2 Spike molecular recognition

Since the beginning of the Covid19 pandemic, many efforts have been devoted to identifying approaches to neutralize SARS-CoV-2 replication within the host cell. A promising strategy to block the infection consists of using a mutant of the human receptor angiotensin-converting enzyme 2 (ACE2) as a decoy to compete with endogenous ACE2 for the binding to the SARS-CoV-2 Spike protein, which decreases the ability of the virus to enter the host cell. Here, using a computational framework based on the 2D Zernike formalism we investigate details of the molecular binding and evaluate the changes in ACE2-Spike binding compatibility upon mutations occurring in the ACE2 side of the molecular interface. We demonstrate the efficacy of our method by comparing our results with experimental binding affinities changes upon ACE2 mutations, separating ones that increase or decrease binding affinity with an Area Under the ROC curve ranging from 0.66 to 0.93, depending on the magnitude of the effects analyzed. Importantly, the iteration of our approach leads to the identification of a set of ACE2 mutants characterized by an increased shape complementarity with Spike. We investigated the physico-chemical properties of these ACE2 mutants and propose them as bona fide candidates for Spike recognition.

Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides

BACKGROUND

Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes is a major concern for their implementation. With the rising antibiotic resistance, antimicrobial peptides (AMPs) have increasingly gained attention as novel therapeutic agents. This challenging design problem requires peptides which meet the multiple constraints of limiting drug-resistance in bacteria, preventing secondary infections from imbalanced microbial flora, and avoiding immune system suppression. AMPs offer a promising, bioinspired design space to targeting antimicrobial activity, but their versatility also requires the curated selection from a combinatorial sequence space. This space is too large for brute-force methods or currently known rational design approaches outside of machine learning. While there has been progress in using the design space to more effectively target AMP activity, a widely applicable approach has been elusive. The lack of transparency in machine learning has limited the advancement of scientific knowledge of how AMPs are related among each other, and the lack of general applicability for fully rational approaches has limited a broader understanding of the design space.

METHODS

Here we combined an evolutionary method with rough set theory, a transparent machine learning approach, for designing antimicrobial peptides (AMPs). Our method achieves the customization of AMPs using supervised learning boundaries. Our system employs in vitro bacterial assays to measure fitness, codon-representation of peptides to gain flexibility of sequence selection in DNA-space with a genetic algorithm and machine learning to further accelerate the process.

RESULTS

We use supervised machine learning and a genetic algorithm to find a peptide active against S. epidermidis, a common bacterial strain for implant infections, with an improved aggregation propensity average for an improved ease of synthesis.

CONCLUSIONS

Our results demonstrate that AMP design can be customized to maintain activity and simplify production. To our knowledge, this is the first time when codon-based genetic algorithms combined with rough set theory methods is used for computational search on peptide sequences.

Novel KCND3 Variant Underlying Nonprogressive Congenital Ataxia or SCA19/22 Disrupt KV4.3 Protein Expression and K+ Currents with Variable Effects on Channel Properties

KCND3 encodes the voltage-gated potassium channel K_V4.3 that is highly expressed in the cerebellum, where it regulates dendritic excitability and calcium influx. Loss-of-function K_V4.3 mutations have been associated with dominant spinocerebellar ataxia (SCA19/22). By targeted NGS sequencing, we identified two novel KCND3 missense variants of the K_V4.3 channel: p.S347W identified in a patient with adult-onset pure cerebellar syndrome and p.W359G detected in a child with congenital nonprogressive ataxia. Neuroimaging showed mild cerebellar atrophy in both patients. We performed a two-electrode voltage-clamp recording of K_V4.3 currents in Xenopus oocytes: both the p.G345V (previously reported in a SCA19/22 family) and p.S347W mutants exhibited reduced peak currents by 50%, while no K+ current was detectable for the p.W359G mutant. We assessed the effect of the mutations on channel gating by measuring steady-state voltage-dependent activation and inactivation properties: no significant alterations were detected in p.G345V and p.S347W disease-associated variants, compared to controls. K_V4.3 expression studies in HEK293T cells showed 53% (p.G345V), 45% (p.S347W) and 75% (p.W359G) reductions in mutant protein levels compared with the wildtype. The present study broadens the spectrum of the known phenotypes and identifies additional variants for KCND3-related disorders, outlining the importance of SCA gene screening in early-onset and congenital ataxia.

Enforced dimerization between XBP1s and ATF6f enhances the protective effects of the UPR in models of neurodegeneration

Alteration to endoplasmic reticulum (ER) proteostasis is observed in a variety of neurodegenerative diseases associated with abnormal protein aggregation. Activation of the unfolded protein response (UPR) enables an adaptive reaction to recover ER proteostasis and cell function. The UPR is initiated by specialized stress sensors that engage gene expression programs through the concerted action of the transcription factors ATF4, ATF6f, and XBP1s. Although UPR signaling is generally studied as unique linear signaling branches, correlative evidence suggests that ATF6f and XBP1s may physically interact to regulate a subset of UPR target genes. In this study, we designed an ATF6f/XBP1s fusion protein termed UPRplus that behaves as a heterodimer in terms of its selective transcriptional activity. Cell-based studies demonstrated that UPRplus has a stronger effect in reducing the abnormal aggregation of mutant huntingtin and α-synuclein when compared to XBP1s or ATF6 alone. We developed a gene transfer approach to deliver UPRplus into the brain using adeno-associated viruses (AAVs) and demonstrated potent neuroprotection in vivo in preclinical models of Parkinson's disease and Huntington's disease. These results support the concept in which directing UPR-mediated gene expression toward specific adaptive programs may serve as a possible strategy to optimize the beneficial effects of the pathway in different disease conditions.

Step-by-step design of proteins for small molecule interaction: A review on recent milestones

Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.

Unraveling the stability landscape of mutations in the SARS-CoV-2 receptor-binding domain

The interaction between the receptor-binding domain (RBD) of the SARS-CoV-2 spike glycoprotein and the ACE2 enzyme is believed to be the entry point of the virus into various cells in the body, including the lungs, heart, liver, and kidneys. The current focus of several therapeutic design efforts explores attempts at affecting the binding potential between the two proteins to limit the activity of the virus and disease progression. In this work, we analyze the stability of the spike protein under all possible single-point mutations in the RBD and computationally explore mutations that can affect the binding with the ACE2 enzyme. We unravel the mutation landscape of the receptor region and assess the toxicity potential of single and multi-point mutations, generating insights for future vaccine efforts on mutations that might further stabilize the spike protein and increase its infectivity. We developed a tool, called SpikeMutator, to construct full atomic protein structures of the mutant spike proteins and shared a database of 3800 single-point mutant structures. We analyzed the recent 65,000 reported spike sequences across the globe and observed the emergence of stable multi-point mutant structures. Using the landscape, we searched through 7.5 million possible 2-point mutation combinations and report that the (R355D K424E) mutation produces one of the strongest spike proteins that therapeutic efforts should investigate for the sake of developing effective vaccines.

Insights into the Molecular Inhibition of the Oncogenic Channel KV10.1 by Globular Toxins

Inhibition of the expression of the human ether-à-go-go (hEAG1 or hK_V10.1) channel is associated with a dramatic reduction in the growth of several cancerous tumors. The modulation of this channel's activity is a promising target for the development of new anticancer drugs. Although some small molecules have shown inhibitory activity against K_V10.1, their lack of specificity has prevented their use in humans. In vitro studies have recently identified a limited number of peptide toxins with proven specificity in their hK_V10.1 channel inhibitory effect. These peptide toxins have become desirable candidates to use as lead compounds to design more potent and specific hK_V10.1 inhibitors. However, the currently available studies lack the atomic resolution needed to characterize the molecular features that favor their binding to hK_V10.1. In this work, we present the first attempt to locate the possible hK_V10.1 binding sites of the animal peptide toxins APETx4, Aa1a, Ap1a, and k-hefutoxin 1, all of which described as hK_V10.1 inhibitors. Our studies incorporated homology modeling to construct a robust three-dimensional (3D) model of hK_V10.1, applied protein docking, and multiscale molecular dynamics techniques to reveal in atomic resolution the toxin-channel interactions. Our approach suggests that some peptide toxins bind in the outer vestibule surrounding the pore of hK_V10.1; it also identified the channel residues Met397 and Asp398 as possible anchors that stabilize the binding of the evaluated toxins. Finally, a description of the possible mechanism for inhibition and gating is presented.

Multiobjective heuristic algorithm for de novo protein design in a quantified continuous sequence space

Protein design usually involves sequence search process and evaluation criteria. Commonly used methods primarily implement the Monte Carlo or simulated annealing algorithm with a single-energy function to obtain ideal solutions, which is often highly time-consuming and limited by the accuracy of the energy function. In this report, we introduce a multiobjective algorithm named Hydra for protein design, which employs two different energy functions to optimize solutions simultaneously and makes use of the latent quantitative relationship between different amino acid types to facilitate the search process. The framework uses two kinds of prior information to transform the original disordered discrete sequence space into a relatively ordered space, and decoy sequences are searched in this ordered space through a multiobjective swarm intelligence algorithm. This algorithm features high accuracy and a high-speed search process. Our method was tested on 40 targets covering different fold classes, which were computationally verified to be well folded, and it experimentally solved the 1UBQ fold by NMR in excellent agreement with the native structure with a backbone RMSD deviation of 1.074 Å. The Hydra software package can be downloaded from: http://www.csbio.sjtu.edu.cn/bioinf/HYDRA/ for academic use.

Systemic lupus erythematosus overlapping dermatomyositis owing to a heterozygous TREX1 Asp130Asn missense mutation

The 3' repair exonuclease 1 (TREX1) gene encodes a nuclear protein with 3' exonuclease activity, and the mutations have been associated with autoimmune diseases. Herein, we performed genetic analysis for the TREX1 gene in 55 patients with systemic lupus erythematosus (SLE). We identified one SLE patient with overlapping dermatomyositis having a heterozygous p.Asp130Asn mutation in the TREX1 gene. The patient had a high level of serum interferon (IFN)-α compared with that in healthy controls and other patients with SLE. In addition, the patient expressed elevated IFN signature genes compared with healthy controls. Our molecular dynamics simulation of the TREX1 protein in a complex with double-stranded DNA revealed that the D130N mutant causes significant changes in the active site's interaction network. One of our cases exhibited a heterozygous TREX1 p.Asp130Asn mutation that contributed to the type I IFN pathway, which may lead to the development of a severe SLE phenotype.

Brazilin Removes Toxic Alpha-Synuclein and Seeding Competent Assemblies from Parkinson Brain by Altering Conformational Equilibrium

Alpha-synuclein (α-syn) fibrils, a major constituent of the neurotoxic Lewy Bodies in Parkinson's disease, form via nucleation dependent polymerization and can replicate by a seeding mechanism. Brazilin, a small molecule derived from red cedarwood trees in Brazil, has been shown to inhibit the fibrillogenesis of amyloid-beta (Aβ) and α-syn as well as remodel mature fibrils and reduce cytotoxicity. Here we test the effects of Brazilin on both seeded and unseeded α-syn fibril formation and show that the natural polyphenol inhibits fibrillogenesis of α-syn by a unique mechanism that alters conformational equilibria in two separate points of the assembly mechanism: Brazilin preserves the natively unfolded state of α-syn by specifically binding to the compact conformation of the α-syn monomer. Brazilin also eliminates seeding competence of α-syn assemblies from Parkinson's disease patient brain tissue, and reduces toxicity of pre-formed assemblies in primary neurons by inducing the formation of large fibril clusters. Molecular docking of Brazilin shows the molecule to interact both with unfolded α-syn monomers and with the cross-β sheet structure of α-syn fibrils. Our findings suggest that Brazilin has substantial potential as a neuroprotective and therapeutic agent for Parkinson's disease.

Identifying structural-functional analogue of GRL0617, the only well-established inhibitor for papain-like protease (PLpro) of SARS-CoV2 from the pool of fungal metabolites using docking and molecular dynamics simulation

Abstract

The non-structural protein (nsp)-3 of SARS-CoV2 coronavirus is sought to be an essential target protein which is also named as papain-like protease (PLpro). This protease cleaves the viral polyprotein, but importantly in human host it also removes ubiquitin-like interferon-stimulated gene 15 protein (ISG15) from interferon responsive factor 3 (IRF3) protein which ultimately downregulates the production of type I interferon leading to weakening of immune response. GRL0617 is the most potent known inhibitor for PLpro that was initially developed for SARS outbreak of 2003. The PLpro of SARS-CoV and CoV2 share 83% sequence identity but interestingly have several identical conserved amino acids that suggests GRL0617 to be an effective inhibitor for PLpro of SARS-CoV2. GRL0617 is a naphthalene-based molecule and interacts with Tyr268 of SARS-CoV2-PLpro (and Tyr269 of SARS-CoV-PLpro). To identify PLpro inhibitors, we prepared a library of secondary metabolites from fungi with aromatic nature and docked them with PLpro of SARS-CoV and SARS-CoV2. We found six hits which interacts with Tyr268 of SARS-CoV2-PLpro (and Tyr269 of SARS-CoV-PLpro). More surprisingly the top hit, Fonsecin, has naphthalene moiety in its structure, which recruits Tyr268 of SARS-CoV2-PLpro (and Tyr269 of SARS-CoV-PLpro) and has binding energy at par with control (GRL0617). Molecular dynamics (MD) simulation showed Fonsecin to interact with Tyr268 of SARS-CoV2-PLpro more efficiently than control (GRL0617) and interacting with a greater number of amino acids in the binding cleft of PLpro.

Graphic abstract

Rational thermostabilisation of four-helix bundle dimeric de novo proteins

The stability of proteins is an important factor for industrial and medical applications. Improving protein stability is one of the main subjects in protein engineering. In a previous study, we improved the stability of a four-helix bundle dimeric de novo protein (WA20) by five mutations. The stabilised mutant (H26L/G28S/N34L/V71L/E78L, SUWA) showed an extremely high denaturation midpoint temperature (Tm). Although SUWA is a remarkably hyperstable protein, in protein design and engineering, it is an attractive challenge to rationally explore more stable mutants. In this study, we predicted stabilising mutations of WA20 by in silico saturation mutagenesis and molecular dynamics simulation, and experimentally confirmed three stabilising mutations of WA20 (N22A, N22E, and H86K). The stability of a double mutant (N22A/H86K, rationally optimised WA20, ROWA) was greatly improved compared with WA20 (ΔTm = 10.6 °C). The model structures suggested that N22A enhances the stability of the α-helices and N22E and H86K contribute to salt-bridge formation for protein stabilisation. These mutations were also added to SUWA and improved its Tm. Remarkably, the most stable mutant of SUWA (N22E/H86K, rationally optimised SUWA, ROSA) showed the highest Tm (129.0 °C). These new thermostable mutants will be useful as a component of protein nanobuilding blocks to construct supramolecular protein complexes.

Predicting cryptic ligand binding sites based on normal modes guided conformational sampling

To greatly expand the druggable genome, fast and accurate predictions of cryptic sites for small molecules binding in target proteins are in high demand. In this study, we have developed a fast and simple conformational sampling scheme guided by normal modes solved from the coarse-grained elastic models followed by atomistic backbone refinement and side-chain repacking. Despite the observations of complex and diverse conformational changes associated with ligand binding, we found that simply sampling along each of the lowest 30 modes is near optimal for adequately restructuring cryptic sites so they can be detected by existing pocket finding programs like fpocket and concavity. We further trained machine-learning protocols to optimize the combination of the sampling-enhanced pocket scores with other dynamic and conservation scores, which only slightly improved the performance. As assessed based on a training set of 84 known cryptic sites and a test set of 14 proteins, our method achieved high accuracy of prediction (with area under the receiver operating characteristic curve >0.8) comparable to the CryptoSite server. Compared with CryptoSite and other methods based on extensive molecular dynamics simulation, our method is much faster (1-2 hours for an average-size protein) and simpler (using only pocket scores), so it is suitable for high-throughput processing of large datasets of protein structures at the genome scale.

Effects of pH on an IDP conformational ensemble explored by molecular dynamics simulation

The conformational ensemble of intrinsically disordered proteins, such as α-synuclein, are responsible for their function and malfunction. Misfolding of α-synuclein can lead to neurodegenerative diseases, and the ability to study their conformations and those of other intrinsically disordered proteins under varying physiological conditions can be crucial to understanding and preventing pathologies. In contrast to well-folded peptides, a consensus feature of IDPs is their low hydropathy and high charge, which makes their conformations sensitive to pH perturbation. We examine a prominent member of this subset of IDPs, α-synuclein, using a divide-and-conquer scheme that provides enhanced sampling of IDP structural ensembles. We constructed conformational ensembles of α-synuclein under neutral (pH ~ 7) and low (pH ~ 3) pH conditions and compared our results with available information obtained from smFRET, SAXS, and NMR studies. Specifically, α-synuclein has been found to in a more compact state at low pH conditions and the structural changes observed are consistent with those from experiments. We also characterize the conformational and dynamic differences between these ensembles and discussed the implication on promoting pathogenic fibril formation. We find that under low pH conditions, neutralization of negatively charged residues leads to compaction of the C-terminal portion of α-synuclein while internal reorganization allows α-synuclein to maintain its overall end-to-end distance. We also observe different levels of intra-protein interaction between three regions of α-synuclein at varying pH and a shift towards more hydrophilic interactions with decreasing pH.

D614G substitution at the hinge region enhances the stability of trimeric SARS-CoV-2 spike protein

Mutations in the spike protein of SARS-CoV-2 are the major causes for the modulation of ongoing COVID-19 infection. Currently, the D614G substitution in the spike protein has become dominant worldwide. It is associated with higher infectivity than the ancestral (D614)variant. We demonstrate using Gaussian network model-based normal mode analysis that the D614G substitution occurs at the hinge region that facilitates domain-domain motions between receptor binding domain and S2 region of the spike protein. Computer-aided mutagenesis and inter-residue energy calculations reveal that contacts involving D614 are energetically frustrated. However, contacts involving G614 are energetically favourable, implying the substitution strengthens residue contacts that are formed within as well as between protomers. We also find that the free energy difference (ΔΔG) between two variants is -2.6 kcal/mol for closed and -2.0 kcal/mol for 1-RBD up conformation. Thus, the thermodynamic stability has increased upon D614G substitution. Whereas the reverse mutation in spike protein structures having G614 substitution has resulted in the free energy differences of 6.6 kcal/mol and 6.3 kcal/mol for closed and 1-RBD up conformations, respectively, indicating that the overall thermodynamic stability has decreased. These results suggest that the D614G substitution modulates the flexibility of spike protein and confers enhanced thermodynamic stability irrespective of conformational states. This data concurs with the known information demonstrating increased availability of the functional form of spikeprotein trimer upon D614G substitution.

P3CMQA: Single-Model Quality Assessment Using 3DCNN with Profile-Based Features

Model quality assessment (MQA), which selects near-native structures from structure models, is an important process in protein tertiary structure prediction. The three-dimensional convolution neural network (3DCNN) was applied to the task, but the performance was comparable to existing methods because it used only atom-type features as the input. Thus, we added sequence profile-based features, which are also used in other methods, to improve the performance. We developed a single-model MQA method for protein structures based on 3DCNN using sequence profile-based features, namely, P3CMQA. Performance evaluation using a CASP13 dataset showed that profile-based features improved the assessment performance, and the proposed method was better than currently available single-model MQA methods, including the previous 3DCNN-based method. We also implemented a web-interface of the method to make it more user-friendly.

Multi-Scale Flexible Fitting of Proteins to Cryo-EM Density Maps at Medium Resolution

Structure determination using cryo-electron microscopy (cryo-EM) medium-resolution density maps is often facilitated by flexible fitting. Avoiding overfitting, adjusting force constants driving the structure to the density map, and emulating complex conformational transitions are major concerns in the fitting. To address them, we develop a new method based on a three-step multi-scale protocol. First, flexible fitting molecular dynamics (MD) simulations with coarse-grained structure-based force field and replica-exchange scheme between different force constants replicas are performed. Second, fitted Cα atom positions guide the all-atom structure in targeted MD. Finally, the all-atom flexible fitting refinement in implicit solvent adjusts the positions of the side chains in the density map. Final models obtained via the multi-scale protocol are significantly better resolved and more reliable in comparison with long all-atom flexible fitting simulations. The protocol is useful for multi-domain systems with intricate structural transitions as it preserves the secondary structure of single domains.

A multiscale coarse-grained model of the SARS-CoV-2 virion

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic. Computer simulations of complete viral particles can provide theoretical insights into large-scale viral processes including assembly, budding, egress, entry, and fusion. Detailed atomistic simulations are constrained to shorter timescales and require billion-atom simulations for these processes. Here, we report the current status and ongoing development of a largely "bottom-up" coarse-grained (CG) model of the SARS-CoV-2 virion. Data from a combination of cryo-electron microscopy (cryo-EM), x-ray crystallography, and computational predictions were used to build molecular models of structural SARS-CoV-2 proteins, which were then assembled into a complete virion model. We describe how CG molecular interactions can be derived from all-atom simulations, how viral behavior difficult to capture in atomistic simulations can be incorporated into the CG models, and how the CG models can be iteratively improved as new data become publicly available. Our initial CG model and the detailed methods presented are intended to serve as a resource for researchers working on COVID-19 who are interested in performing multiscale simulations of the SARS-CoV-2 virion.

Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences

Increasing numbers of protein interactions have been identified in high-throughput experiments, but only a small proportion have solved structures. Recently, sequence coevolution-based approaches have led to a breakthrough in predicting monomer protein structures and protein interaction interfaces. Here, we address the challenges of large-scale interaction prediction at residue resolution with a fast alignment concatenation method and a probabilistic score for the interaction of residues. Importantly, this method (EVcomplex2) is able to assess the likelihood of a protein interaction, as we show here applied to large-scale experimental datasets where the pairwise interactions are unknown. We predict 504 interactions de novo in the E. coli membrane proteome, including 243 that are newly discovered. While EVcomplex2 does not require available structures, coevolving residue pairs can be used to produce structural models of protein interactions, as done here for membrane complexes including the Flagellar Hook-Filament Junction and the Tol/Pal complex.

Our understanding of the residue-level details of protein interactions remains incomplete. Here, the authors show sequence coevolution can be used to infer interacting proteins with residue-level details, including predicting 467 interactions de novo in the Escherichia coli cell envelope proteome.

Predicting new protein conformations from molecular dynamics simulation conformational landscapes and machine learning

Molecular dynamics (MD) simulations are a popular method of studying protein structure and function, but are unable to reliably sample all relevant conformational space in reasonable computational timescales. A range of enhanced sampling methods are available that can improve conformational sampling, but these do not offer a complete solution. We present here a proof-of-principle method of combining MD simulation with machine learning to explore protein conformational space. An autoencoder is used to map snapshots from MD simulations onto a user-defined conformational landscape defined by principal components analysis or specific structural features, and we show that we can predict, with useful accuracy, conformations that are not present in the training data. This method offers a new approach to the prediction of new low energy/physically realistic structures of conformationally dynamic proteins and allows an alternative approach to enhanced sampling of MD simulations.

Characterization of Light-Induced, Short-Lived Interacting Radicals in the Active Site of Flavoprotein Ferredoxin-NADP+ Oxidoreductase

Radicals of flavin adenine dinucleotide (FAD), as well as tyrosine and tryptophan, are widely involved as key reactive intermediates during electron-transfer (ET) reactions in flavoproteins. Due to the high reactivity of these species and their corresponding short lifetime, characterization of these intermediates in functional processes of flavoproteins is usually challenging but can be achieved by ultrafast spectroscopic studies of light-activatable flavoproteins. In ferredoxin-NADP⁺ oxidoreductase from Bacillus subtilis (BsFNR), fluorescence of the FAD cofactor that very closely interacts with a neighboring tyrosine residue (Tyr50) is strongly quenched. Here we study short-lived photoproducts of this enzyme and its variants, with Tyr50 replaced by tryptophan or glycine. Using time-resolved fluorescence and absorption spectroscopies, we show that, upon the excitation of WT BsFNR, ultrafast ET from Tyr50 to the excited FAD cofactor occurs in ∼260 fs, an order of magnitude faster than the decay by charge recombination, facilitating the characterization of the reaction intermediates in the charge-separated state with respect to other recently studied systems. These studies are corroborated by experiments on the Y50W mutant protein, which yield photoproducts qualitatively similar to those observed in other tryptophan-bearing flavoproteins. By combining the experimental results with molecular dynamics simulations and quantum mechanics calculations, we investigate in detail the effects of protein environment and relaxations on the spectral properties of those radical intermediates and demonstrate that the spectral features of radical anionic FAD are highly sensitive to its environment, and in particular to the dynamics and nature of the counterions formed in the photoproducts. Altogether, comprehensive characterizations are provided for important radical intermediates that are generally involved in functional processes of flavoproteins.

Improved Sampling Strategies for Protein Model Refinement Based on Molecular Dynamics Simulation

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. These methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore the conformational space more broadly. Based on the insights of this analysis, we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here.

Concatemers to re-investigate the role of α5 in α4β2 nicotinic receptors

Nicotinic acetylcholine receptors (nAChRs) are pentameric ion channels expressed in the central nervous systems. nAChRs containing the α4, β2 and α5 subunits are specifically involved in addictive processes, but their functional architecture is poorly understood due to the intricacy of assembly of these subunits. Here we constrained the subunit assembly by designing fully concatenated human α4β2 and α4β2α5 receptors and characterized their properties by two-electrodes voltage-clamp electrophysiology in Xenopus oocytes. We found that α5-containing nAChRs are irreversibly blocked by methanethiosulfonate (MTS) reagents through a covalent reaction with a cysteine present only in α5. MTS-block experiments establish that the concatemers are expressed in intact form at the oocyte surface, but that reconstitution of nAChRs from loose subunits show inefficient and highly variable assembly of α5 with α4 and β2. Mutational analysis shows that the concatemers assemble both in clockwise and anticlockwise orientations, and that α5 does not contribute to ACh binding from its principal (+) site. Reinvestigation of suspected α5-ligands such as galantamine show no specific effect on α5-containing concatemers. Analysis of the α5-D398N mutation that is linked to smoking and lung cancer shows no significant effect on the electrophysiological function, suggesting that its effect might arise from alteration of other cellular processes. The concatemeric strategy provides a well-characterized platform for mechanistic analysis and screening of human α5-specific ligands.