Improved prediction of protein side-chain conformations with SCWRL4

Evolution of the Automatic Rhodopsin Modeling (ARM) Protocol

In recent years, photoactive proteins such as rhodopsins have become a common target for cutting-edge research in the field of optogenetics. Alongside wet-lab research, computational methods are also developing rapidly to provide the necessary tools to analyze and rationalize experimental results and, most of all, drive the design of novel systems. The Automatic Rhodopsin Modeling (ARM) protocol is focused on providing exactly the necessary computational tools to study rhodopsins, those being either natural or resulting from mutations. The code has evolved along the years to finally provide results that are reproducible by any user, accurate and reliable so as to replicate experimental trends. Furthermore, the code is efficient in terms of necessary computing resources and time, and scalable in terms of both number of concurrent calculations as well as features. In this review, we will show how the code underlying ARM achieved each of these properties.

Strain and rupture of HIV-1 capsids during uncoating

SignificanceThe mature capsids of HIV-1 are transiently stable complexes that self-assemble around the viral genome during maturation, and uncoat to release preintegration complexes that archive a double-stranded DNA copy of the virus in the host cell genome. However, a detailed view of how HIV cores rupture remains lacking. Here, we elucidate the physical properties involved in capsid rupture using a combination of large-scale all-atom molecular dynamics simulations and cryo-electron tomography. We find that intrinsic strain on the capsid forms highly correlated patterns along the capsid surface, along which cracks propagate. Capsid rigidity also increases with high strain. Our findings provide fundamental insight into viral capsid uncoating.

AbAdapt: an adaptive approach to predicting antibody-antigen complex structures from sequence

Motivation

The scoring of antibody-antigen docked poses starting from unbound homology models has not been systematically optimized for a large and diverse set of input sequences.

Results

To address this need, we have developed AbAdapt, a webserver that accepts antibody and antigen sequences, models their 3D structures, predicts epitope and paratope, and then docks the modeled structures using two established docking engines (Piper and Hex). Each of the key steps has been optimized by developing and training new machine-learning models. The sequences from a diverse set of 622 antibody-antigen pairs with known structure were used as inputs for leave-one-out cross-validation. The final set of cluster representatives included at least one 'Adequate' pose for 550/622 (88.4%) of the queries. The median (interquartile range) ranks of these 'Adequate' poses were 22 (5-77). Similar results were obtained on a holdout set of 100 unrelated antibody-antigen pairs. When epitopes were repredicted using docking-derived features for specific antibodies, the median ROC AUC increased from 0.679 to 0.720 in cross-validation and from 0.694 to 0.730 in the holdout set.

Availability and implementation

AbAdapt and related data are available at https://sysimm.org/abadapt/.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Challenges and Advantages of Accounting for Backbone Flexibility in Prediction of Protein-Protein Complexes

Predicting protein binding is a core problem of computational biophysics. That this objective can be partly achieved with some amount of success using docking algorithms based on rigid protein models is remarkable, although going further requires allowing for protein flexibility. However, accurately capturing the conformational changes upon binding remains an enduring challenge for docking algorithms. Here, we adapt our Upside folding model, where side chains are represented as multi-position beads, to explore how flexibility may impact predictions of protein-protein complexes. Specifically, the Upside model is used to investigate where backbone flexibility helps, which types of interactions are important, and what is the impact of coarse graining. These efforts also shed light on the relative challenges posed by folding and docking. After training the Upside energy function for docking, the model is competitive with the established all-atom methods. However, allowing for backbone flexibility during docking is generally detrimental, as the presence of comparatively minor (3-5 Å) deviations relative to the docked structure has a large negative effect on performance. While this issue appears to be inherent to current forcefield-guided flexible docking methods, systems involving the co-folding of flexible loops such as antibody-antigen complexes represent an interesting exception. In this case, binding is improved when backbone flexibility is allowed using the Upside model.

Evidence for a trap-and-flip mechanism in a proton-dependent lipid transporter

Transport of lipids across membranes is fundamental for diverse biological pathways in cells. Multiple ion-coupled transporters take part in lipid translocation, but their mechanisms remain largely unknown. Major facilitator superfamily (MFS) lipid transporters play central roles in cell wall synthesis, brain development and function, lipids recycling, and cell signaling. Recent structures of MFS lipid transporters revealed overlapping architectural features pointing towards a common mechanism. Here we used cysteine disulfide trapping, molecular dynamics simulations, mutagenesis analysis, and transport assays in vitro and in vivo, to investigate the mechanism of LtaA, a proton-dependent MFS lipid transporter essential for lipoteichoic acid synthesis in the pathogen Staphylococcus aureus. We reveal that LtaA displays asymmetric lateral openings with distinct functional relevance and that cycling through outward- and inward-facing conformations is essential for transport activity. We demonstrate that while the entire amphipathic central cavity of LtaA contributes to lipid binding, its hydrophilic pocket dictates substrate specificity. We propose that LtaA catalyzes lipid translocation by a ‘trap-and-flip’ mechanism that might be shared among MFS lipid transporters.

LtaA catalyzes glycolipid translocation by a ‘trap-and-flip’ mechanism, pointing to a shared mechanistic model among MFS lipid transporters. Asymmetric lateral openings allow access of the entire lipid substrate to the amphipathic central cavity.

Allosteric Determinants of the SARS-CoV-2 Spike Protein Binding with Nanobodies: Examining Mechanisms of Mutational Escape and Sensitivity of the Omicron Variant

Structural and biochemical studies have recently revealed a range of rationally engineered nanobodies with efficient neutralizing capacity against the SARS-CoV-2 virus and resilience against mutational escape. In this study, we performed a comprehensive computational analysis of the SARS-CoV-2 spike trimer complexes with single nanobodies Nb6, VHH E, and complex with VHH E/VHH V nanobody combination. We combined coarse-grained and all-atom molecular simulations and collective dynamics analysis with binding free energy scanning, perturbation-response scanning, and network centrality analysis to examine mechanisms of nanobody-induced allosteric modulation and cooperativity in the SARS-CoV-2 spike trimer complexes with these nanobodies. By quantifying energetic and allosteric determinants of the SARS-CoV-2 spike protein binding with nanobodies, we also examined nanobody-induced modulation of escaping mutations and the effect of the Omicron variant on nanobody binding. The mutational scanning analysis supported the notion that E484A mutation can have a significant detrimental effect on nanobody binding and result in Omicron-induced escape from nanobody neutralization. Our findings showed that SARS-CoV-2 spike protein might exploit the plasticity of specific allosteric hotspots to generate escape mutants that alter response to binding without compromising activity. The network analysis supported these findings showing that VHH E/VHH V nanobody binding can induce long-range couplings between the cryptic binding epitope and ACE2-binding site through a broader ensemble of communication paths that is less dependent on specific mediating centers and therefore may be less sensitive to mutational perturbations of functional residues. The results suggest that binding affinity and long-range communications of the SARS-CoV-2 complexes with nanobodies can be determined by structurally stable regulatory centers and conformationally adaptable hotspots that are allosterically coupled and collectively control resilience to mutational escape.

PSTPIP1-LYP phosphatase interaction: structural basis and implications for autoinflammatory disorders

Mutations in the adaptor protein PSTPIP1 cause a spectrum of autoinflammatory diseases, including PAPA and PAMI; however, the mechanism underlying these diseases remains unknown. Most of these mutations lie in PSTPIP1 F-BAR domain, which binds to LYP, a protein tyrosine phosphatase associated with arthritis and lupus. To shed light on the mechanism by which these mutations generate autoinflammatory disorders, we solved the structure of the F-BAR domain of PSTPIP1 alone and bound to the C-terminal homology segment of LYP, revealing a novel mechanism of recognition of Pro-rich motifs by proteins in which a single LYP molecule binds to the PSTPIP1 F-BAR dimer. The residues R228, D246, E250, and E257 of PSTPIP1 that are mutated in immunological diseases directly interact with LYP. These findings link the disruption of the PSTPIP1/LYP interaction to these diseases, and support a critical role for LYP phosphatase in their pathogenesis.

Conformational Flexibility and Local Frustration in the Functional States of the SARS-CoV-2 Spike B.1.1.7 and B.1.351 Variants: Mutation-Induced Allosteric Modulation Mechanism of Functional Dynamics and Protein Stability

Structural and functional studies of the SARS-CoV-2 spike proteins have recently determined distinct functional states of the B.1.1.7 and B.1.351 spike variants, providing a molecular framework for understanding the mechanisms that link the effect of mutations with the enhanced virus infectivity and transmissibility. A detailed dynamic and energetic analysis of these variants was undertaken in the present work to quantify the effects of different mutations on functional conformational changes and stability of the SARS-CoV-2 spike protein. We employed the efficient and accurate coarse-grained (CG) simulations of multiple functional states of the D614G mutant, B.1.1.7 and B.1.351 spike variants to characterize conformational dynamics of the SARS-CoV-2 spike proteins and identify dynamic signatures of the functional regions that regulate transitions between the closed and open forms. By combining molecular simulations with full atomistic reconstruction of the trajectories and the ensemble-based mutational frustration analysis, we characterized how the intrinsic flexibility of specific spike regions can control functional conformational changes required for binding with the host-cell receptor. Using the residue-based mutational scanning of protein stability, we determined protein stability hotspots and identified potential energetic drivers favoring the receptor-accessible open spike states for the B.1.1.7 and B.1.351 spike variants. The results suggested that modulation of the energetic frustration at the inter-protomer interfaces can serve as a mechanism for allosteric couplings between mutational sites and the inter-protomer hinges of functional motions. The proposed mechanism of mutation-induced energetic frustration may result in greater adaptability and the emergence of multiple conformational states in the open form. This study suggested that SARS-CoV-2 B.1.1.7 and B.1.351 variants may leverage the intrinsic plasticity of functional regions in the spike protein for mutation-induced modulation of protein dynamics and allosteric regulation to control binding with the host cell receptor.

Hexameric rings of the scaffolding protein CheW enhance response sensitivity and cooperativity in Escherichia coli chemoreceptor arrays

The Escherichia coli chemoreceptor array is a supramolecular assembly that enables cells to respond to extracellular cues dynamically and with great precision and sensitivity. In the array, transmembrane receptors organized as trimers of dimers are connected at their cytoplasmic tips by hexameric rings of alternating subunits of the kinase CheA and the scaffolding protein CheW (CheA-CheW rings). Interactions of CheW molecules with the members of receptor trimers not directly bound to CheA-CheW rings may lead to the formation of hexameric CheW rings in the chemoreceptor array. Here, we detected such CheW rings with a cellular cysteine-directed cross-linking assay and explored the requirements for their formation and their participation in array assembly. We found that CheW ring formation varied with cellular CheW abundance, depended on the presence of receptors capable of a trimer-of-dimers arrangement, and did not require CheA. Cross-linking studies of a CheA~CheW fusion protein incapable of forming homomeric CheW oligomers demonstrated that CheW rings were not essential for the assembly of CheA-containing arrays. Förster resonance energy transfer (FRET)-based kinase assays of arrays containing variable amounts of CheW rings revealed that CheW rings enhanced the cooperativity and the sensitivity of the responses to attractants. We propose that six-membered CheW rings provide the additional interconnectivity required for optimal signaling and gradient tracking performance by chemosensory arrays.

OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors

Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer library, which may have limitations on their accuracies and usages. In this study, we report an open-source toolkit for protein side-chain modeling, named OPUS-Rota4. It consists of three modules: OPUS-RotaNN2, which predicts protein side-chain dihedral angles; OPUS-RotaCM, which measures the distance and orientation information between the side chain of different residue pairs and OPUS-Fold2, which applies the constraints derived from the first two modules to guide side-chain modeling. OPUS-Rota4 adopts the dihedral angles predicted by OPUS-RotaNN2 as its initial states, and uses OPUS-Fold2 to refine the side-chain conformation with the side-chain contact map constraints derived from OPUS-RotaCM. Therefore, we convert the side-chain modeling problem into a side-chain contact map prediction problem. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to include other differentiable energy terms. OPUS-Rota4 also provides a platform in which the side-chain conformation can be dynamically adjusted under the influence of other processes. We apply OPUS-Rota4 on 15 FM predictions submitted by AlphaFold2 on CASP14, the results show that the side chains modeled by OPUS-Rota4 are closer to their native counterparts than those predicted by AlphaFold2 (e.g. the residue-wise RMSD for all residues and core residues are 0.588 and 0.472 for AlphaFold2, and 0.535 and 0.407 for OPUS-Rota4).

Analysis of Integrin αIIb Subunit Dynamics Reveals Long-Range Effects of Missense Mutations on Calf Domains

Integrin α_IIbβ₃, a glycoprotein complex expressed at the platelet surface, is involved in platelet aggregation and contributes to primary haemostasis. Several integrin α_IIbβ₃ polymorphisms prevent the aggregation that causes haemorrhagic syndromes, such as Glanzmann thrombasthenia (GT). Access to 3D structure allows understanding the structural effects of polymorphisms related to GT. In a previous analysis using Molecular Dynamics (MD) simulations of α_IIbCalf-1 domain structure, it was observed that GT associated with single amino acid variation affects distant loops, but not the mutated position. In this study, experiments are extended to Calf-1, Thigh, and Calf-2 domains. Two loops in Calf-2 are unstructured and therefore are modelled expertly using biophysical restraints. Surprisingly, MD revealed the presence of rigid zones in these loops. Detailed analysis with structural alphabet, the Proteins Blocks (PBs), allowed observing local changes in highly flexible regions. The variant P741R located at C-terminal of Calf-1 revealed that the Calf-2 presence did not affect the results obtained with isolated Calf-1 domain. Simulations for Calf-1 + Calf-2, and Thigh + Calf-1 variant systems are designed to comprehend the impact of five single amino acid variations in these domains. Distant conformational changes are observed, thus highlighting the potential role of allostery in the structural basis of GT.

Side-chain Packing Using SE(3)-Transformer

Predicting protein side-chains is important for both protein structure prediction and protein design. Modeling approaches to predict side-chains such as SCWRL4 have become one of the most widely used tools of its type due to fast and highly accurate predictions. Motivated by the recent success of AlphaFold2 in CASP14, our group adapted a 3D equivariant neural network architecture to predict protein side-chain conformations, specifically within a protein-protein interface, a problem that has not been fully addressed by AlphaFold2.

Benchmarking of structure refinement methods for protein complex models

Protein structure docking is the process in which the quaternary structure of a protein complex is predicted from individual tertiary structures of the protein subunits. Protein docking is typically performed in two main steps. The subunits are first docked while keeping them rigid to form the complex, which is then followed by structure refinement. Structure refinement is crucial for a practical use of computational protein docking models, as it is aimed for correcting conformations of interacting residues and atoms at the interface. Here, we benchmarked the performance of eight existing protein structure refinement methods in refinement of protein complex models. We show that the fraction of native contacts between subunits is by far the most straightforward metric to improve. However, backbone dependent metrics, based on the Root Mean Square Deviation proved more difficult to improve via refinement.

Systematic profiling and identification of the peptide-mediated interactions between human Yes-associated protein and its partners in esophageal cancer

Human Yes-associated protein (YAP) is involved in the Hippo signaling pathway and serves as a coactivator to modulate gene expression, which contains a transactivation domain (TD) responsible for binding to the downstream TEA domain family (TEAD) of transcription factors and two WW1/2 domains that recognize the proline-rich motifs (PRMs) present in a variety of upstream protein partners through peptide-mediated interactions (PMIs). The downstream YAP TD-TEAD interactions are closely associated with gastric cancer, and a number of therapeutic agents have been developed to target the interactions. In contrast, the upstream YAP WW1/2-partner interactions are thought to be involved in esophageal cancer but still remain largely unexplored. Here, we attempted to elucidate the complicated PMIs between the YAP WW1/2 domains and various PRMs of YAP-interacting proteins. A total of 106 peptide segments carrying the class I WW-binding motif [P/L]Px[Y/P] were extracted from 22 partner candidates, which are potential recognition sites of YAP WW1/2 domains. Structural and energetic analyses of the intermolecular interactions between the domains and peptides created a systematic domain-peptide binding profile, from which a number of biologically functional PMIs were identified and then substantiated in vitro using fluorescence spectroscopy assays. It is revealed that: (a) The sequence requirement for the partner recognition site binding to YAP WW1/2 domains is a decapeptide segment that contains a core PRM motif as well as two three-residue extensions from each side of the motif; the core motif and extended sections are responsible for the binding stability and recognition specificity of domain-peptide interaction, respectively. (b) There is an exquisite difference in the recognition specificity of the two domains; the LPxP and PPxP appear to more prefer WW1 than WW2, whereas the WW2 can bind more effectively to LPxY and PPxY than WW1. (c) WW2 generally exhibits a higher affinity to the panel of recognition site candidates than WW1. In addition, a number of partner peptides were found as promising recognition sites of the two domains and/or to have a good selectivity between the two domains. For example, the DVL1 peptide was determined to have moderate affinity to WW2 and strong selectivity for WW2 over WW1. Hydrogen bonds play a central role in selectivity.

Toward a Computational NMR Procedure for Modeling Dipeptide Side-Chain Conformation

Theoretical relationships between the vicinal spin–spin coupling constants (SSCCs) and the χ₁ torsion angles have been studied to predict the conformations of protein side chains. An efficient computational procedure is developed to obtain the conformation of dipeptides through theoretical and experimental SSCCs, Karplus equations, and quantum chemistry methods, and it is applied to three aliphatic hydrophobic residues (Val, Leu, and Ile). Three models are proposed: unimodal-static, trimodal-static-stepped, and trimodal-static-trigonal, where the most important factors are incorporated (coupled nuclei, nature and orientation of the substituents, and local geometric properties). Our results are validated by comparison with NMR and X-ray empirical data described in the literature, obtaining successful results on the 29 residues considered. Using out trimodal residue treatment, it is possible to detect and resolve residues with a simple conformation and those with two or three staggered conformers. In four residues, a deeper analysis explains that they do not have a unique conformation and that the population of each conformation plays an important role.

The Advantages of EPR Spectroscopy in Exploring Diamagnetic Metal Ion Binding and Transfer Mechanisms in Biological Systems

Electron paramagnetic resonance (EPR) spectroscopy has emerged as an ideal biophysical tool to study complex biological processes. EPR spectroscopy can follow minor conformational changes in various proteins as a function of ligand or protein binding or interactions with high resolution and sensitivity. Resolving cellular mechanisms, involving small ligand binding or metal ion transfer, is not trivial and cannot be studied using conventional biophysical tools. In recent years, our group has been using EPR spectroscopy to study the mechanism underlying copper ion transfer in eukaryotic and prokaryotic systems. This mini-review focuses on our achievements following copper metal coordination in the diamagnetic oxidation state, Cu(I), between biomolecules. We discuss the conformational changes induced in proteins upon Cu(I) binding, as well as the conformational changes induced in two proteins involved in Cu(I) transfer. We also consider how EPR spectroscopy, together with other biophysical and computational tools, can identify the Cu(I)-binding sites. This work describes the advantages of EPR spectroscopy for studying biological processes that involve small ligand binding and transfer between intracellular proteins.

De novo protein design by an energy function based on series expansion in distance and orientation dependence

MOTIVATION

Despite many successes, de novo protein design is not yet a solved problem as its success rate remains low. The low success rate is largely because we do not yet have an accurate energy function for describing the solvent-mediated interaction between amino acid residues in a protein chain. Previous studies showed that an energy function based on series expansions with its parameters optimized for side-chain and loop conformations can lead to one of the most accurate methods for side chain (OSCAR) and loop prediction (LEAP). Following the same strategy, we developed an energy function based on series expansions with the parameters optimized in four separate stages (recovering single-residue types without and with orientation dependence, selecting loop decoys and maintaining the composition of amino acids). We tested the energy function for de novo design by using Monte Carlo simulated annealing.

RESULTS

The method for protein design (OSCAR-Design) is found to be as accurate as OSCAR and LEAP for side-chain and loop prediction, respectively. In de novo design, it can recover native residue types ranging from 38% to 43% depending on test sets, conserve hydrophobic/hydrophilic residues at ∼75%, and yield the overall similarity in amino acid compositions at more than 90%. These performance measures are all statistically significantly better than several protein design programs compared. Moreover, the largest hydrophobic patch areas in designed proteins are near or smaller than those in native proteins. Thus, an energy function based on series expansion can be made useful for protein design.

AVAILABILITY AND IMPLEMENTATION

The Linux executable version is freely available for academic users at http://zhouyq-lab.szbl.ac.cn/resources/.

GABAA Receptor Subunit Composition Drives Its Sensitivity to the Insecticide Fipronil

Fipronil (FPN) is a worldwide-used neurotoxic insecticide, targeting, and blocking GABA_A receptors (GABA_ARs). Beyond its efficiency on insect GABA_ARs, FPN causes neurotoxic effects in humans and mammals. Here, we investigated the mode of action of FPN on mammalian α6-containing GABA_ARs to understand its inhibitory effects on GABA-induced currents, as a function of the synaptic or extrasynaptic localization of GABA_ARs. We characterized the effects of FPN by electrophysiology using Xenopus oocytes which were microtransplanted with cerebellum membranes or injected with α6β3, α6β3γ2S (synaptic), and α6β3δ (extrasynaptic) cDNAs. At micromolar concentrations, FPN dose-dependently inhibited cerebellar GABA currents. FPN acts as a non-competitive antagonist on ternary receptors. Surprisingly, the inhibition of GABA-induced currents was partial for extra-synaptic (α6β3δ) and binary (α6β3) receptors, while synaptic α6β3γ2S receptors were fully blocked, indicating that the complementary γ or δ subunit participates in FPN-GABA_AR interaction. FPN unexpectedly behaved as a positive modulator on β3 homopentamers. These data show that FPN action is driven by the subunit composition of GABA_ARs-highlighting the role of the complementary subunit-and thus their localization within a physiological synapse. We built a docking model of FPN on GABA_ARs, which reveals two putative binding sites. This is consistent with a double binding mode of FPN on GABA_ARs, possibly one being of high affinity and the other of low affinity. Physiologically, the γ/δ subunit incorporation drives its inhibitory level and has important significance for its toxicity on the mammalian nervous system, especially in acute exposure.

Physics-based protein structure refinement in the era of artificial intelligence

Protein structure refinement is the last step in protein structure prediction pipelines. Physics-based refinement via molecular dynamics (MD) simulations has made significant progress during recent years. During CASP14, we tested a new refinement protocol based on an improved sampling strategy via MD simulations. MD simulations were carried out at an elevated temperature (360 K). An optimized use of biasing restraints and the use of multiple starting models led to enhanced sampling. The new protocol generally improved the model quality. In comparison with our previous protocols, the CASP14 protocol showed clear improvements. Our approach was successful with most initial models, many based on deep learning methods. However, we found that our approach was not able to refine machine-learning models from the AlphaFold2 group, often decreasing already high initial qualities. To better understand the role of refinement given new types of models based on machine-learning, a detailed analysis via MD simulations and Markov state modeling is presented here. We continue to find that MD-based refinement has the potential to improve AI predictions. We also identified several practical issues that make it difficult to realize that potential. Increasingly important is the consideration of inter-domain and oligomeric contacts in simulations; the presence of large kinetic barriers in refinement pathways also continues to present challenges. Finally, we provide a perspective on how physics-based refinement could continue to play a role in the future for improving initial predictions based on machine learning-based methods.

Paradigmatic De Novo GRIN1 Variants Recapitulate Pathophysiological Mechanisms Underlying GRIN1-Related Disorder Clinical Spectrum

Background: GRIN-related disorders (GRD), the so-called grinpathies, is a group of rare encephalopathies caused by mutations affecting GRIN genes (mostly GRIN1, GRIN2A and GRIN2B genes), which encode for the GluN subunit of the N-methyl D-aspartate (NMDA) type ionotropic glutamate receptors. A growing number of functional studies indicate that GRIN-encoded GluN1 subunit disturbances can be dichotomically classified into gain- and loss-of-function, although intermediate complex scenarios are often present. Methods: In this study, we aimed to delineate the structural and functional alterations of GRIN1 disease-associated variants, and their correlations with clinical symptoms in a Spanish cohort of 15 paediatric encephalopathy patients harbouring these variants. Results: Patients harbouring GRIN1 disease-associated variants have been clinically deeply-phenotyped. Further, using computational and in vitro approaches, we identified different critical checkpoints affecting GluN1 biogenesis (protein stability, subunit assembly and surface trafficking) and/or NMDAR biophysical properties, and their association with GRD clinical symptoms. Conclusions: Our findings show a strong correlation between GRIN1 variants-associated structural and functional outcomes. This structural-functional stratification provides relevant insights of genotype-phenotype association, contributing to future precision medicine of GRIN1-related encephalopathies.

3D Interaction Homology: Computational Titration of Aspartic Acid, Glutamic Acid and Histidine Can Create pH-Tunable Hydropathic Environment Maps

Aspartic acid, glutamic acid and histidine are ionizable residues occupying various protein environments and perform many different functions in structures. Their roles are tied to their acid/base equilibria, solvent exposure, and backbone conformations. We propose that the number of unique environments for ASP, GLU and HIS is quite limited. We generated maps of these residue's environments using a hydropathic scoring function to record the type and magnitude of interactions for each residue in a 2703-protein structural dataset. These maps are backbone-dependent and suggest the existence of new structural motifs for each residue type. Additionally, we developed an algorithm for tuning these maps to any pH, a potentially useful element for protein design and structure building. Here, we elucidate the complex interplay between secondary structure, relative solvent accessibility, and residue ionization states: the degree of protonation for ionizable residues increases with solvent accessibility, which in turn is notably dependent on backbone structure.

How cis-Acyltransferase Assembly-Line Ketosynthases Gatekeep for Processed Polyketide Intermediates

With the redefinition of polyketide synthase (PKS) modules, a new appreciation of their most downstream domain, the ketosynthase (KS), is emerging. In addition to performing its well-established role of generating a carbon-carbon bond between an acyl-CoA building block and a growing polyketide, it may gatekeep against incompletely processed intermediates. Here, we investigate 739 KSs from 92 primarily actinomycete, cis-acyltransferase assembly lines. When KSs were separated into 16 families based on the chemistries at the α- and β-carbons of their polyketide substrates, a comparison of 32 substrate tunnel residues revealed unique sequence fingerprints. Surprisingly, additional fingerprints were detected when the chemistry at the γ-carbon was considered. Representative KSs were modeled bound to their natural polyketide substrates to better understand observed patterns, such as the substitution of a tryptophan by a smaller residue to accommodate an l-α-methyl group or the substitution of four smaller residues by larger ones to make better contact with a primer unit or diketide. Mutagenesis of a conserved glutamine in a KS within a model triketide synthase indicates that the substrate tunnel is sensitive to alteration and that engineering this KS to accept unnatural substrates may require several mutations.

In Silico Analysis of Glucose Oxidase from Aspergillus niger: Potential Cysteine Mutation Sites for Enhancing Protein Stability

Glucose oxidase (GOx) holds considerable advantages for various applications. Nevertheless, the thermal instability of the enzyme remains a grand challenge, impeding the success in applications outside the well-controlled laboratories, particularly in practical bioelectronics. Many strategies to modify GOx to achieve better thermal stability have been proposed. However, modification of this enzyme by adding extra disulfide bonds is yet to be explored. This work describes the in silico bioengineering of GOx from Aspergillus niger by judiciously analyzing characteristics of disulfide bonds found in the Top8000 protein database, then scanning for amino acid residue pairs that are suitable to be replaced with cysteines in order to establish disulfide bonds. Next, we predicted and assessed the mutant GOx models in terms of disulfide bond quality (bond length and α angles), functional impact by means of residue conservation, and structural impact as indicated by Gibbs free energy. We found eight putative residue pairs that can be engineered to form disulfide bonds. Five of these are located in less conserved regions and, therefore, are unlikely to have a deleterious impact on functionality. Finally, two mutations, Pro149Cys and His158Cys, showed potential for stabilizing the protein structure as confirmed by a structure-based stability analysis tool. The findings in this study highlight the opportunity of using disulfide bond modification as a new alternative technique to enhance the thermal stability of GOx.

Modeling DNA Opening in the Eukaryotic Transcription Initiation Complexes via Coarse-Grained Models

Recently, the molecular mechanisms of transcription initiation have been intensively studied. Especially, the cryo-electron microscopy revealed atomic structure details in key states in the eukaryotic transcription initiation. Yet, the dynamic processes of the promoter DNA opening in the pre-initiation complex remain obscured. In this study, based on the three cryo-electron microscopic yeast structures for the closed, open, and initially transcribing complexes, we performed multiscale molecular dynamics (MD) simulations to model structures and dynamic processes of DNA opening. Combining coarse-grained and all-atom MD simulations, we first obtained the atomic model for the DNA bubble in the open complexes. Then, in the MD simulation from the open to the initially transcribing complexes, we found a previously unidentified intermediate state which is formed by the bottleneck in the fork loop 1 of Pol II: The loop opening triggered the escape from the intermediate, serving as a gatekeeper of the promoter DNA opening. In the initially transcribing complex, the non-template DNA strand passes a groove made of the protrusion, the lobe, and the fork of Rpb2 subunit of Pol II, in which several positively charged and highly conserved residues exhibit key interactions to the non-template DNA strand. The back-mapped all-atom models provided further insights on atomistic interactions such as hydrogen bonding and can be used for future simulations.

Molecular Docking Simulation Studies Identifies Potential Natural Product Derived-Antiwolbachial Compounds as Filaricides against Onchocerciasis

Onchocerciasis is the leading cause of blindness and severe skin lesions which remain a major public health problem, especially in tropical areas. The widespread use of antibiotics and the long duration required for effective treatment continues to add to the increasing global menace of multi-resistant pathogens. Onchocerca volvulus harbors the endosymbiont bacteria Wolbachia, essential for the normal development of embryos, larvae and long-term survival of the adult worm, O. volvulus. We report here results of using structure-based drug design (SBDD) approach aimed at identifying potential novel Wolbachia inhibitors from natural products against the Wolbachia surface protein (WSP). The protein sequence of the WSP with UniProtKB identifier Q0RAI4 was used to model the three-dimensional (3D) structure via homology modelling techniques using three different structure-building algorithms implemented in Modeller, I-TASSER and Robetta. Out of the 15 generated models of WSP, one was selected as the most reasonable quality model which had 82, 15.5, 1.9 and 0.5% of the amino acid residues in the most favored regions, additionally allowed regions, generously allowed regions and disallowed regions, respectively, based on the Ramachandran plot. High throughput virtual screening was performed via Autodock Vina with a library comprising 42,883 natural products from African and Chinese databases, including 23 identified anti-Onchocerca inhibitors. The top six compounds comprising ZINC000095913861, ZINC000095486235, ZINC000035941652, NANPDB4566, acetylaleuritolic acid and rhemannic acid had binding energies of −12.7, −11.1, −11.0, −11, −10.3 and −9.5 kcal/mol, respectively. Molecular dynamics simulations including molecular mechanics Poisson-Boltzmann (MMPBSA) calculations reinforced the stability of the ligand-WSP complexes and plausible binding mechanisms. The residues Arg45, Tyr135, Tyr148 and Phe195 were predicted as potential novel critical residues required for ligand binding in pocket 1. Acetylaleuritolic acid and rhemannic acid (lantedene A) have previously been shown to possess anti-onchocercal activity. This warrants the need to evaluate the anti-WSP activity of the identified molecules. The study suggests the exploitation of compounds which target both pockets 1 and 2, by investigating their potential for effective depletion of Wolbachia. These compounds were predicted to possess reasonably good pharmacological profiles with insignificant toxicity and as drug-like. The compounds were computed to possess biological activity including antibacterial, antiparasitic, anthelmintic and anti-rickettsials. The six natural products are potential novel antiwolbachial agents with insignificant toxicities which can be explored further as filaricides for onchocerciasis.

Resolving distance variations by single-molecule FRET and EPR spectroscopy using rotamer libraries

Förster resonance energy transfer (FRET) and electron paramagnetic resonance (EPR) spectroscopy are complementary techniques for quantifying distances in the nanometer range. Both approaches are commonly employed for probing the conformations and conformational changes of biological macromolecules based on site-directed fluorescent or paramagnetic labeling. FRET can be applied in solution at ambient temperature and thus provides direct access to dynamics, especially if used at the single-molecule level, whereas EPR requires immobilization or work at cryogenic temperatures but provides data that can be more reliably used to extract distance distributions. However, a combined analysis of the complementary data from the two techniques has been complicated by the lack of a common modeling framework. Here, we demonstrate a systematic analysis approach based on rotamer libraries for both FRET and EPR labels to predict distance distributions between two labels from a structural model. Dynamics of the fluorophores within these distance distributions are taken into account by diffusional averaging, which improves the agreement with experiment. Benchmarking this methodology with a series of surface-exposed pairs of sites in a structured protein domain reveals that the lowest resolved distance differences can be as small as ∼0.25 nm for both techniques, with quantitative agreement between experimental and simulated transfer efficiencies within a range of ±0.045. Rotamer library analysis thus establishes a coherent way of treating experimental data from EPR and FRET and provides a basis for integrative structural modeling, including studies of conformational distributions and dynamics of biological macromolecules using both techniques.

Redesigning an antibody H3 loop by virtual screening of a small library of human germline-derived sequences

The design of superior biologic therapeutics, including antibodies and engineered proteins, involves optimizing their specific ability to bind to disease-related molecular targets. Previously, we developed and applied the Assisted Design of Antibody and Protein Therapeutics (ADAPT) platform for virtual affinity maturation of antibodies (Vivcharuk et al. in PLoS One 12(7):e0181490, 10.1371/journal.pone.0181490, 2017). However, ADAPT is limited to point mutations of hot-spot residues in existing CDR loops. In this study, we explore the possibility of wholesale replacement of the entire H3 loop with no restriction to maintain the parental loop length. This complements other currently published studies that sample replacements for the CDR loops L1, L2, L3, H1 and H2. Given the immense sequence space theoretically available to H3, we focused on the virtual grafting of over 5000 human germline-derived H3 sequences from the IGMT/LIGM database increasing the diversity of the sequence space when compared to using crystalized H3 loop sequences. H3 loop conformations are generated and scored to identify optimized H3 sequences. Experimental testing of high-ranking H3 sequences grafted into the framework of the bH1 antibody against human VEGF-A led to the discovery of multiple hits, some of which had similar or better affinities relative to the parental antibody. In over 75% of the tested designs, the re-designed H3 loop contributed favorably to overall binding affinity. The hits also demonstrated good developability attributes such as high thermal stability and no aggregation. Crystal structures of select re-designed H3 variants were solved and indicated that although some deviations from predicted structures were seen in the more solvent accessible regions of the H3 loop, they did not significantly affect predicted affinity scores.

Allosteric Control of Structural Mimicry and Mutational Escape in the SARS-CoV-2 Spike Protein Complexes with the ACE2 Decoys and Miniprotein Inhibitors: A Network-Based Approach for Mutational Profiling of Binding and Signaling

We developed a computational framework for comprehensive and rapid mutational scanning of binding energetics and residue interaction networks in the SARS-CoV-2 spike protein complexes. Using this approach, we integrated atomistic simulations and conformational landscaping of the SARS-CoV-2 spike protein complexes with ensemble-based mutational screening and network modeling to characterize mechanisms of structure-functional mimicry and resilience toward mutational escape by the ACE2 protein decoy and de novo designed miniprotein inhibitors. A detailed analysis of structural plasticity of the SARS-CoV-2 spike proteins obtained from atomistic simulations of conformational landscapes and sequence-based profiling of the disorder propensities revealed the intrinsically flexible regions that harbor key functional sites targeted by circulating variants. The conservation of collective dynamics in the SARS-CoV-2 spike protein complexes showed that mutational escape positions are important for modulation of functional motions and that mutational changes in these sites can alter allosteric interaction networks. Through mutational profiling of binding and allosteric propensities in the SARS-CoV-2 spike protein complexes, we identified the key binding and regulatory hotspots that collectively determine functional response and resilience of miniproteins to mutational variants. The results suggest that binding affinities and allosteric signatures of the SARS-CoV-2 complexes can be determined by dynamic crosstalk between structurally stable regulatory centers and conformationally adaptable allosteric hotspots that collectively control the resilience toward mutational escape. This may underlie a mechanism in which moderate perturbations in the mutational escape positions can induce global allosteric changes and alter functional protein response by modulating signaling in the residue interaction networks.

Integrin-based mechanosensing through conformational deformation

Conversion of integrins from low to high affinity states, termed activation, is important in biological processes, including immunity, hemostasis, angiogenesis, and embryonic development. Integrin activation is regulated by large-scale conformational transitions from closed, low affinity states to open, high affinity states. Although it has been suggested that substrate stiffness shifts the conformational equilibrium of integrin and governs its unbinding, here, we address the role of integrin conformational activation in cellular mechanosensing. Comparison of wild-type versus activating mutants of integrin αVβ3 show that activating mutants shift cell spreading, focal adhesion kinase activation, traction stress, and force on talin toward high stiffness values at lower stiffness. Although all activated integrin mutants showed equivalent binding affinity for soluble ligands, the β3 S243E mutant showed the strongest shift in mechanical responses. To understand this behavior, we used coarse-grained computational models derived from molecular level information. The models predicted that wild-type integrin αVβ3 displaces under force and that activating mutations shift the required force toward lower values, with S243E showing the strongest effect. Cellular stiffness sensing thus correlates with computed effects of force on integrin conformation. Together, these data identify a role for force-induced integrin conformational deformation in cellular mechanosensing.

Pro219 is an electrostatic color determinant in the light-driven sodium pump KR2

Color tuning in animal and microbial rhodopsins has attracted the interest of many researchers, as the color of their common retinal chromophores is modulated by the amino acid residues forming the chromophore cavity. Critical cavity amino acid residues are often called “color switches”, as the rhodopsin color is effectively tuned through their substitution. Well-known color switches are the L/Q and A/TS switches located in the C and G helices of the microbial rhodopsin structure respectively. Recently, we reported on a third G/P switch located in the F helix of the light-driven sodium pumps of KR2 and JsNaR causing substantial spectral red-shifts in the latter with respect to the former. In order to investigate the molecular-level mechanism driving such switching function, here we present an exhaustive mutation, spectroscopic and computational investigation of the P219X mutant set of KR2. To do so, we study the changes in the absorption band of the 19 possible mutants and construct, semi-automatically, the corresponding hybrid quantum mechanics/molecular mechanics models. We found that the P219X feature a red-shifted light absorption with the only exception of P219R. The analysis of the corresponding models indicate that the G/P switch induces red-shifting variations via electrostatic interactions, while replacement-induced chromophore geometrical (steric) distortions play a minor role. However, the same analysis indicates that the P219R blue-shifted variant has a more complex origin involving both electrostatic and steric changes accompanied by protonation state and hydrogen bond networks modifications. These results make it difficult to extract simple rules or formulate theories for predicting how a switch operates without considering the atomistic details and environmental consequences of the side chain replacement.

Nakajima, Pedraza-González et al. provide a comprehensive investigation of amino acid mutations at position 219 of the sodium pump rhodopsin, KR2, and their role in the color tuning of the retinal chromophore. They prepared P219X (X= A, C, D, E, F, G, H, I, K, L, M, N, Q, R, S, T, V, W, Y) mutants of KR2, and find that all mutants are red-shifted, except for P219R, highlighting its role as a color determinant in the light-driven pump KR2.

Atomistic Simulations and In Silico Mutational Profiling of Protein Stability and Binding in the SARS-CoV-2 Spike Protein Complexes with Nanobodies: Molecular Determinants of Mutational Escape Mechanisms

Structure-functional studies have recently revealed a spectrum of diverse high-affinity nanobodies with efficient neutralizing capacity against SARS-CoV-2 virus and resilience against mutational escape. In this study, we combine atomistic simulations with the ensemble-based mutational profiling of binding for the SARS-CoV-2 S-RBD complexes with a wide range of nanobodies to identify dynamic and binding affinity fingerprints and characterize the energetic determinants of nanobody-escaping mutations. Using an in silico mutational profiling approach for probing the protein stability and binding, we examine dynamics and energetics of the SARS-CoV-2 complexes with single nanobodies Nb6 and Nb20, VHH E, a pair combination VHH E + U, a biparatopic nanobody VHH VE, and a combination of the CC12.3 antibody and VHH V/W nanobodies. This study characterizes the binding energy hotspots in the SARS-CoV-2 protein and complexes with nanobodies providing a quantitative analysis of the effects of circulating variants and escaping mutations on binding that is consistent with a broad range of biochemical experiments. The results suggest that mutational escape may be controlled through structurally adaptable binding hotspots in the receptor-accessible binding epitope that are dynamically coupled to the stability centers in the distant binding epitope targeted by VHH U/V/W nanobodies. This study offers a plausible mechanism in which through cooperative dynamic changes, nanobody combinations and biparatopic nanobodies can elicit the increased binding affinity response and yield resilience to common escape mutants.

DIPEND: An Open-Source Pipeline to Generate Ensembles of Disordered Segments Using Neighbor-Dependent Backbone Preferences

Ensemble-based structural modeling of flexible protein segments such as intrinsically disordered regions is a complex task often solved by selection of conformers from an initial pool based on their conformity to experimental data. However, the properties of the conformational pool are crucial, as the sampling of the conformational space should be sufficient and, in the optimal case, relatively uniform. In other words, the ideal sampling is both efficient and exhaustive. To achieve this, specialized tools are usually necessary, which might not be maintained in the long term, available on all platforms or flexible enough to be tweaked to individual needs. Here, we present an open-source and extendable pipeline to generate initial protein structure pools for use with selection-based tools to obtain ensemble models of flexible protein segments. Our method is implemented in Python and uses ChimeraX, Scwrl4, Gromacs and neighbor-dependent backbone distributions compiled and published previously by the Dunbrack lab. All these tools and data are publicly available and maintained. Our basic premise is that by using residue-specific, neighbor-dependent Ramachandran distributions, we can enhance the efficient exploration of the relevant region of the conformational space. We have also provided a straightforward way to bias the sampling towards specific conformations for selected residues by combining different conformational distributions. This allows the consideration of a priori known conformational preferences such as in the case of preformed structural elements. The open-source and modular nature of the pipeline allows easy adaptation for specific problems. We tested the pipeline on an intrinsically disordered segment of the protein Cd3ϵ and also a single-alpha helical (SAH) region by generating conformational pools and selecting ensembles matching experimental data using the CoNSEnsX+ server.

Molecular insight into the affinity, specificity and cross-reactivity of systematic hepatocellular carcinoma RALT interaction profile with human receptor tyrosine kinases

The ErbB family of receptor tyrosine kinases (RTKs) contains four members: EGFR, ErbB2, ErbB3 and ErbB4; they are involved in the tumorigenesis of diverse cancers and can be inhibited natively by receptor-associated late transducer (RALT), a negative feedback regulator of ErbB signaling in human hepatocytes and hepatocellular carcinoma. Although the biological effects of RALT on EGFR kinase have been widely documented previously, the binding behavior of RALT to other ErbB/RTK kinases still remains largely unexplored. Here, the intermolecular interactions of RALT ErbB-binding region (EBR) as well as its functional sections and peptide segments with ErbBs and other human RTKs were systematically investigated at molecular and structural levels, from which we were able to identify those potential kinase targets of RALT protein, and to profile the affinity, specificity and cross-reactivity of RALT EBR domain and its sub-regions against various RTKs. It is revealed that RALT can target all the four ErbB kinases with high affinity for EGFR/ErbB2/ErbB4 and moderate affinity for ErbB3, but generally exhibits modest affinity to other RTKs, albeit few kinases such as LTK, EPHB6, MET and MUSK were also top-ranked as the unexpected targets of RALT. Peptide segments covering the key binding regions of RALT EBR domain were identified with computational alanine scanning, which were then optimized to obtain a number of designed peptide mutants with improved selectivity between different top-ranked RTKs.

Improving the organic solvent resistance of lipase a from Bacillus subtilis in water-ethanol solvent through rational surface engineering

Given that lipase is an enzyme applicable in various industrial fields and water-miscible organic solvents are important reaction media for developing industrial-scale biocatalysis, a structure-based strategy was explored to stabilize lipase A from Bacillus subtilis in a water-ethanol cosolvent. Site-directed mutagenesis of ethanol-interacting sites resulted in 4 mutants, i.e., Ser16Gly, Ala38Gly, Ala38Thr, and Leu108Asn, which were stable in 50% ethanol and had up to 1.8-fold higher stability than the wild-type. In addition, Leu108Asn was more thermostable at 45 °C than the wild type. The results discussed in this study not only provide insights into strategies for enzyme engineering to improve organic solvent resistance but also suggest perspectives on pioneering routes for constructing enzyme-based biorefineries to produce value-added fuels and chemicals.

Mutational analysis to explore long-range allosteric couplings involved in a pentameric channel receptor pre-activation and activation

Pentameric ligand-gated ion channels (pLGICs) mediate chemical signaling through a succession of allosteric transitions that are yet not completely understood as intermediate states remain poorly characterized by structural approaches. In a previous study on the prototypic bacterial proton-gated channel GLIC, we generated several fluorescent sensors of the protein conformation that report a fast transition to a pre-active state, which precedes the slower process of activation with pore opening. Here, we explored the phenotype of a series of allosteric mutations, using simultaneous steady-state fluorescence and electrophysiological measurements over a broad pH range. Our data, fitted to a three-state Monod-Wyman-Changeux model, show that mutations at the subunit interface in the extracellular domain (ECD) principally alter pre-activation, while mutations in the lower ECD and in the transmembrane domain principally alter activation. We also show that propofol alters both transitions. Data are discussed in the framework of transition pathways generated by normal mode analysis (iModFit). It further supports that pre-activation involves major quaternary compaction of the ECD, and suggests that activation involves principally a reorganization of a ‘central gating region’ involving a contraction of the ECD β-sandwich and the tilt of the channel lining M2 helix.

Subset of Fluorophores Is Responsible for Radiation Brightening in Viromimetic Particles

In certain conditions, dye-conjugated icosahedral virus shells exhibit suppression of concentration quenching. The recently observed radiation brightening at high fluorophore densities has been attributed to coherent emission, i.e., to a cooperative process occurring within a subset of the virus-supported fluorophores. Until now, the distribution of fluorophores among potential conjugation sites and the nature of the active subset remained unknown. With the help of mass spectrometry and molecular dynamics simulations, we found which conjugation sites in the brome mosaic virus capsid are accessible to fluorophores. Reactive external surface lysines but also those at the lumenal interface where the coat protein N-termini are located showed virtually unrestricted access to dyes. The third type of labeled lysines was situated at the intercapsomeric interfaces. Through limited proteolysis of flexible N-termini, it was determined that dyes bound to them are unlikely to be involved in the radiation brightening effect. At the same time, specific labeling of genetically inserted cysteines on the exterior capsid surface alone did not lead to radiation brightening. The results suggest that lysines situated within the more rigid structural part of the coat protein provide the chemical environments conducive to radiation brightening, and we discuss some of the characteristics of these environments.

Frustrated peptide chains at the fibril tip control the kinetics of growth of amyloid-β fibrils

Amyloid fibrillization is an exceedingly complex process in which incoming peptide chains bind to the fibril while concertedly folding. The coupling between folding and binding is not fully understood. We explore the molecular pathways of association of Aβ40 monomers to fibril tips by combining time-resolved in situ scanning probe microscopy with molecular modeling. The comparison between experimental and simulation results shows that a complex supported by nonnative contacts is present in the equilibrium structure of the fibril tip and impedes fibril growth in a supersaturated solution. The unraveling of this frustrated state determines the rate of fibril growth. The kinetics of growth of freshly cut fibrils, in which the bulk fibril structure persists at the tip, complemented by molecular simulations, indicate that this frustrated complex comprises three or four monomers in nonnative conformations and likely is contained on the top of a single stack of peptide chains in the fibril structure. This pathway of fibril growth strongly deviates from the common view that the conformational transformation of each captured peptide chain is templated by the previously arrived peptide. The insights into the ensemble structure of the frustrated complex may guide the search for suppressors of Aβ fibrillization. The uncovered dynamics of coupled structuring and assembly during fibril growth are more complex than during the folding of most globular proteins, as they involve the collective motions of several peptide chains that are not guided by a funneled energy landscape.

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.

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.

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?