Improved prediction of protein side-chain conformations with SCWRL4

Phenotypic, molecular, and functional characterization of COQ7-related primary CoQ10 deficiency: Hypomorphic variants and two distinct disease entities

Primary coenzyme Q10 (CoQ10) deficiency is a group of inborn errors of metabolism caused by defects in CoQ10 biosynthesis. Biallelic pathogenic variants in COQ7, encoding mitochondrial 5-demethoxyubiquinone hydroxylase, have been reported in nine patients from seven families. We identified five new patients with COQ7-related primary CoQ10 deficiency, performed clinical assessment of the patients, and studied the functional effects of current and previously reported COQ7 variants and potential treatment options. The main clinical features included a neonatal-onset presentation with severe neuromuscular, cardiorespiratory and renal involvement and a late-onset disease presenting with progressive neuropathy, lower extremity weakness, abnormal gait, and variable developmental delay. Baker's yeast orthologue of COQ7, CAT5, is required for growth on oxidative carbon sources and cat5Δ strain demonstrates oxidative growth defect. Expression of wild-type CAT5 could completely rescue the defect; however, yeast CAT5 harboring equivalent human pathogenic variants could not. Interestingly, cat5Δ yeast harboring p.Arg57Gln (equivalent to human p.Arg54Gln), p.Arg112Trp (equivalent to p.Arg107Trp), p.Ile69Asn (equivalent to p.Ile66Asn) and combination of p.Lys108Met and p.Leu116Pro (equivalent to the complex allele p.[Thr103Met;Leu111Pro]) partially rescued the growth defects, indicating these variants are hypomorphic alleles. Supplementation with 2,4 dihydroxybenzoic acid (2,4-diHB) rescued the growth defect of both the leaky and severe mutants. Overexpression of COQ8 and 2,4-diHB supplementation synergistically restored oxidative growth and respiratory defect. Overall, we define two distinct disease presentations of COQ7-related disorder with emerging genotype-phenotype correlation and validate the use of the yeast model for functional studies of COQ7 variants.

Sequestration of histidine kinases by non-cognate response regulators establishes a threshold level of stimulation for bacterial two-component signaling

Bacterial two-component systems (TCSs) consist of a sensor histidine kinase (HK) that perceives a specific signal, and a cognate response regulator (RR) that modulates the expression of target genes. Positive autoregulation improves TCS sensitivity to stimuli, but may trigger disproportionately large responses to weak signals, compromising bacterial fitness. Here, we combine experiments and mathematical modelling to reveal a general design that prevents such disproportionate responses: phosphorylated HKs (HK~Ps) can be sequestered by non-cognate RRs. We study five TCSs of Mycobacterium tuberculosis and find, for all of them, non-cognate RRs that show higher affinity than cognate RRs for HK~Ps. Indeed, in vitro assays show that HK~Ps preferentially bind higher affinity non-cognate RRs and get sequestered. Mathematical modelling indicates that this sequestration would introduce a 'threshold' stimulus strength for eliciting responses, thereby preventing responses to weak signals. Finally, we construct tunable expression systems in Mycobacterium bovis BCG to show that higher affinity non-cognate RRs suppress responses in vivo.

ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing

Computational protein design has been demonstrated to be the most powerful tool in the last few years among protein designing and repacking tasks. In practice, these two tasks are strongly related but often treated separately. Besides, state-of-the-art deep-learning-based methods cannot provide interpretability from an energy perspective, affecting the accuracy of the design. Here we propose a new systematic approach, including both a posterior probability and a joint probability parts, to solve the two essential questions once for all. This approach takes the physicochemical property of amino acids into consideration and uses the joint probability model to ensure the convergence between structure and amino acid type. Our results demonstrated that this method could generate feasible, high-confidence sequences with low-energy side conformations. The designed sequences can fold into target structures with high confidence and maintain relatively stable biochemical properties. The side chain conformation has a significantly lower energy landscape without delegating to a rotamer library or performing the expensive conformational searches. Overall, we propose an end-to-end method that combines the advantages of both deep learning and energy-based methods. The design results of this model demonstrate high efficiency, and precision, as well as a low energy state and good interpretability.

Structural modeling of antibody variable regions using deep learning-progress and perspectives on drug discovery

AlphaFold2 has hallmarked a generational improvement in protein structure prediction. In particular, advances in antibody structure prediction have provided a highly translatable impact on drug discovery. Though AlphaFold2 laid the groundwork for all proteins, antibody-specific applications require adjustments tailored to these molecules, which has resulted in a handful of deep learning antibody structure predictors. Herein, we review the recent advances in antibody structure prediction and relate them to their role in advancing biologics discovery.

Phosphorylation at Ser65 modulates ubiquitin conformational dynamics

Ubiquitin phosphorylation at Ser65 increases the population of a rare C-terminally retracted (CR) conformation. Transition between the Major and CR ubiquitin conformations is critical for promoting mitochondrial degradation. The mechanisms by which the Major and CR conformations of Ser65-phosphorylated (pSer65) ubiquitin interconvert, however, remain unresolved. Here, we perform all-atom molecular dynamics simulations using the string method with swarms of trajectories to calculate the lowest free-energy path between these two conformers. Our analysis reveals the existence of a Bent intermediate in which the C-terminal residues of the β5 strand shift to resemble the CR conformation, while pSer65 retains contacts resembling the Major conformation. This stable intermediate was reproduced in well-tempered metadynamics calculations but was less stable for a Gln2Ala mutant that disrupts contacts with pSer65. Lastly, dynamical network modeling reveals that the transition from the Major to CR conformations involves a decoupling of residues near pSer65 from the adjacent β1 strand.

Solvent Accessibility Promotes Rotamer Errors during Protein Modeling with Major Side-Chain Prediction Programs

Side-chain rotamer prediction is one of the most critical late stages in protein 3D structure building. Highly advanced and specialized algorithms (e.g., FASPR, RASP, SCWRL4, and SCWRL4v) optimize this process by use of rotamer libraries, combinatorial searches, and scoring functions. We seek to identify the sources of key rotamer errors as a basis for correcting and improving the accuracy of protein modeling going forward. In order to evaluate the aforementioned programs, we process 2496 high-quality single-chained all-atom filtered 30% homology protein 3D structures and use discretized rotamer analysis to compare original with calculated structures. Among 513,024 filtered residue records, increased amino acid residue-dependent rotamer errors─associated in particular with polar and charged amino acid residues (ARG, LYS, and GLN)─clearly correlate with increased amino acid residue solvent accessibility and an increased residue tendency toward the adoption of non-canonical off rotamers which modeling programs struggle to predict accurately. Understanding the impact of solvent accessibility now appears key to improved side-chain prediction accuracies.

Transient water wires mediate selective proton transport in designed channel proteins

Selective proton transport through proteins is essential for forming and using proton gradients in cells. Protons are conducted along hydrogen-bonded 'wires' of water molecules and polar side chains, which, somewhat surprisingly, are often interrupted by dry apolar stretches in the conduction pathways, inferred from static protein structures. Here we hypothesize that protons are conducted through such dry spots by forming transient water wires, often highly correlated with the presence of the excess protons in the water wire. To test this hypothesis, we performed molecular dynamics simulations to design transmembrane channels with stable water pockets interspersed by apolar segments capable of forming flickering water wires. The minimalist designed channels conduct protons at rates similar to viral proton channels, and they are at least 106-fold more selective for H+ over Na+. These studies inform the mechanisms of biological proton conduction and the principles for engineering proton-conductive materials.

BeEM: fast and faithful conversion of mmCIF format structure files to PDB format

BACKGROUND

Although mmCIF is the current official format for deposition of protein and nucleic acid structures to the protein data bank (PDB) database, the legacy PDB format is still the primary supported format for many structural bioinformatics tools. Therefore, reliable software to convert mmCIF structure files to PDB files is needed. Unfortunately, existing conversion programs fail to correctly convert many mmCIF files, especially those with many atoms and/or long chain identifies.

RESULTS

This study proposed BeEM, which converts any mmCIF format structure files to PDB format. BeEM conversion faithfully retains all atomic and chain information, including chain IDs with more than 2 characters, which are not supported by any existing mmCIF to PDB converters. The conversion speed of BeEM is at least ten times faster than existing converters such as MAXIT and Phenix. Part of the reason for the speed improvement is the avoidance of conversion between numerical values and text strings.

CONCLUSION

BeEM is a fast and accurate tool for mmCIF-to-PDB format conversion, which is a common procedure in structural biology. The source code is available under the BSD licence at https://github.com/kad-ecoli/BeEM/ .

An end-to-end deep learning method for protein side-chain packing and inverse folding

Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to tackle this problem, but their speed or accuracy is still unsatisfactory. To address this, we present AttnPacker, a deep learning (DL) method for directly predicting protein side-chain coordinates. Unlike existing methods, AttnPacker directly incorporates backbone 3D geometry to simultaneously compute all side-chain coordinates without delegating to a discrete rotamer library or performing expensive conformational search and sampling steps. This enables a significant increase in computational efficiency, decreasing inference time by over 100× compared to the DL-based method DLPacker and physics-based RosettaPacker. Tested on the CASP13 and CASP14 native and nonnative protein backbones, AttnPacker computes physically realistic side-chain conformations, reducing steric clashes and improving both rmsd and dihedral accuracy compared to state-of-the-art methods SCWRL4, FASPR, RosettaPacker, and DLPacker. Different from traditional PSCP approaches, AttnPacker can also codesign sequences and side chains, producing designs with subnative Rosetta energy and high in silico consistency.

Functional impact and molecular binding modes of drugs that target the PI3K isoform p110δ

Targeting the PI3K isoform p110δ against B cell malignancies is at the mainstay of PI3K inhibitor (PI3Ki) development. Therefore, we generated isogenic cell lines, which express wild type or mutant p110δ, for assessing the potency, isoform-selectivity and molecular interactions of various PI3Ki chemotypes. The affinity pocket mutation I777M maintains p110δ activity in the presence of idelalisib, as indicated by intracellular AKT phosphorylation, and rescues cell functions such as p110δ-dependent cell viability. Resistance owing to this substitution consistently affects the potency of p110δ-selective in contrast to most multi-targeted PI3Ki, thus distinguishing usually propeller-shaped and typically flat molecules. Accordingly, molecular dynamics simulations indicate that the I777M substitution disturbs conformational flexibility in the specificity or affinity pockets of p110δ that is necessary for binding idelalisib or ZSTK474, but not copanlisib. In summary, cell-based and molecular exploration provide comparative characterization of currently developed PI3Ki and structural insights for future PI3Ki design.

Markov State Models and Perturbation-Based Approaches Reveal Distinct Dynamic Signatures and Hidden Allosteric Pockets in the Emerging SARS-Cov-2 Spike Omicron Variants Complexes with the Host Receptor: The Interplay of Dynamics and Convergent Evolution Modulates Allostery and Functional Mechanisms

The new generation of SARS-CoV-2 Omicron variants displayed a significant growth advantage and the increased viral fitness by acquiring convergent mutations, suggesting that the immune pressure can promote convergent evolution leading to the sudden acceleration of SARS-CoV-2 evolution. In the current study, we combined structural modeling, extensive microsecond MD simulations and Markov state models to characterize conformational landscapes and identify specific dynamic signatures of the SARS-CoV-2 spike complexes with the host receptor ACE2 for the recently emerged highly transmissible XBB.1, XBB.1.5, BQ.1, and BQ.1.1 Omicron variants. Microsecond simulations and Markovian modeling provided a detailed characterization of the conformational landscapes and revealed the increased thermodynamic stabilization of the XBB.1.5 subvariant which is contrasted to more dynamic BQ.1 and BQ.1.1 subvariants. Despite considerable structural similarities, Omicron mutations can induce unique dynamic signatures and specific distributions of conformational states. The results suggested that variant-specific changes of conformational mobility in the functional interfacial loops of the spike receptor binding domain can be fine-tuned through cross-talk between convergent mutations thereby providing an evolutionary path for modulation of immune escape. By combining atomistic simulations and Markovian modeling analysis with perturbation-based approaches, we determined important complementary roles of convergent mutation sites as effectors and receivers of allosteric signaling involved in modulating conformational plasticity at the binding interface and regulating allosteric responses. This study also characterized the dynamics-induced evolution of allosteric pockets in the Omicron complexes that revealed hidden allosteric pockets and suggested that convergent mutation sites could control evolution and distribution of allosteric pockets through modulation of conformational plasticity in the flexible adaptable regions. Through integrative computational approaches, this investigation provides a systematic analysis and comparison of the effects of Omicron subvariants on conformational dynamics and allosteric signaling in the complexes with the ACE2 receptor.

For Table of Contents Use Only

Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling a balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility.

Computational structural-based GPCR optimization for user-defined ligand: Implications for the development of biosensors

Organisms have developed effective mechanisms to sense the external environment. Human-designed biosensors exploit this natural optimization, where different biological machinery have been adapted to detect the presence of user-defined molecules. Specifically, the pheromone pathway in the model organism Saccharomyces cerevisiae represents a suitable candidate as a synthetic signaling system. Indeed, it expresses just one G-Protein Coupled Receptor (GPCR), Ste2, able to recognize pheromone and initiate the expression of pheromone-dependent genes. To date, the standard procedure to engineer this system relies on the substitution of the yeast GPCR with another one and on the modification of the yeast G-protein to bind the inserted receptor. Here, we propose an innovative computational procedure, based on geometrical and chemical optimization of protein binding pockets, to select the amino acid substitutions required to make the native yeast GPCR able to recognize a user-defined ligand. This procedure would allow the yeast to recognize a wide range of ligands, without a-priori knowledge about a GPCR recognizing them or the corresponding G protein. We used Monte Carlo simulations to design on Ste2 a binding pocket able to recognize epinephrine, selected as a test ligand. We validated Ste2 mutants via molecular docking and molecular dynamics. We verified that the amino acid substitutions we identified make Ste2 able to accommodate and remain firmly bound to epinephrine. Our results indicate that we sampled efficiently the huge space of possible mutants, proposing such a strategy as a promising starting point for the development of a new kind of S.cerevisiae-based biosensors.

Probing Conformational Landscapes of Binding and Allostery in the SARS-CoV-2 Omicron Variant Complexes Using Microsecond Atomistic Simulations and Perturbation-Based Profiling Approaches: Hidden Role of Omicron Mutations as Modulators of Allosteric Signaling and Epistatic Relationships

In this study, we systematically examine the conformational dynamics, binding and allosteric communications in the Omicron BA.1, BA.2, BA.3 and BA.4/BA.5 complexes with the ACE2 host receptor using molecular dynamics simulations and perturbation-based network profiling approaches. Microsecond atomistic simulations provided a detailed characterization of the conformational landscapes and revealed the increased thermodynamic stabilization of the BA.2 variant which is contrasted with the BA.4/BA.5 variants inducing a significant mobility of the complexes. Using ensemble-based mutational scanning of binding interactions, we identified binding affinity and structural stability hotspots in the Omicron complexes. Perturbation response scanning and network-based mutational profiling approaches probed the effect of the Omicron variants on allosteric communications. The results of this analysis revealed specific roles of Omicron mutations as "plastic and evolutionary adaptable" modulators of binding and allostery which are coupled to the major regulatory positions through interaction networks. Through perturbation network scanning of allosteric residue potentials in the Omicron variant complexes, which is performed in the background of the original strain, we identified that the key Omicron binding affinity hotspots N501Y and Q498R could mediate allosteric interactions and epistatic couplings. Our results suggested that the synergistic role of these hotspots in controlling stability, binding and allostery can enable for compensatory balance of fitness tradeoffs with conformationally and evolutionary adaptable immune-escape Omicron mutations. Through integrative computational approaches, this study provides a systematic analysis of the effects of Omicron mutations on thermodynamics, binding and allosteric signaling in the complexes with ACE2 receptor. The findings support a mechanism in which Omicron mutations can evolve to balance thermodynamic stability and conformational adaptability in order to ensure proper tradeoff between stability, binding and immune escape.

Multi-site-specific isotopic labeling accelerates high-resolution structural investigations of pathogenic huntingtin exon-1

Huntington's disease neurodegeneration occurs when the number of consecutive glutamines in the huntingtin exon-1 (HTTExon1) exceeds a pathological threshold of 35. The sequence homogeneity of HTTExon1 reduces the signal dispersion in NMR spectra, hampering its structural characterization. By simultaneously introducing three isotopically labeled glutamines in a site-specific manner in multiple concatenated samples, 18 glutamines of a pathogenic HTTExon1 with 36 glutamines were unambiguously assigned. Chemical shift analyses indicate the α-helical persistence in the homorepeat and the absence of an emerging toxic conformation around the pathological threshold. Using the same type of samples, the recognition mechanism of Hsc70 molecular chaperone has been investigated, indicating that it binds to the N17 region of HTTExon1, inducing the partial unfolding of the poly-Q. The proposed strategy facilitates high-resolution structural and functional studies in low-complexity regions.

High Accuracy Prediction of PROTAC Complex Structures

The design of PROteolysis-TArgeting Chimeras (PROTACs) requires bringing an E3 ligase into proximity with a target protein to modulate the concentration of the latter through its ubiquitination and degradation. Here, we present a method for generating high-accuracy structural models of E3 ligase-PROTAC-target protein ternary complexes. The method is dependent on two computational innovations: adding a "silent" convolution term to an efficient protein-protein docking program to eliminate protein poses that do not have acceptable linker conformations and clustering models of multiple PROTACs that use the same E3 ligase and target the same protein. Results show that the largest consensus clusters always have high predictive accuracy and that the ensemble of models can be used to predict the dissociation rate and cooperativity of the ternary complex that relate to the degrading activity of the PROTAC. The method is demonstrated by applications to known PROTAC structures and a blind test involving PROTACs against BRAF mutant V600E. The results confirm that PROTACs function by stabilizing a favorable interaction between the E3 ligase and the target protein but do not necessarily exploit the most energetically favorable geometry for interaction between the proteins.

Coarse-Grained Molecular Simulations and Ensemble-Based Mutational Profiling of Protein Stability in the Different Functional Forms of the SARS-CoV-2 Spike Trimers: Balancing Stability and Adaptability in BA.1, BA.2 and BA.2.75 Variants

Evolutionary and functional studies have suggested that the emergence of Omicron variants can be determined by multiple fitness tradeoffs including immune escape, binding affinity, conformational plasticity, protein stability, and allosteric modulation. In this study, we embarked on a systematic comparative analysis of the conformational dynamics, electrostatics, protein stability, and allostery in the different functional states of spike trimers for BA.1, BA.2, and BA.2.75 variants. Using efficient and accurate coarse-grained simulations and atomistic reconstruction of the ensembles, we examined the conformational dynamics of the spike trimers that agree with the recent functional studies, suggesting that BA.2.75 trimers are the most stable among these variants. A systematic mutational scanning of the inter-protomer interfaces in the spike trimers revealed a group of conserved structural stability hotspots that play a key role in the modulation of functional dynamics and are also involved in the inter-protomer couplings through local contacts and interaction networks with the Omicron mutational sites. The results of mutational scanning provided evidence that BA.2.75 trimers are more stable than BA.2 and comparable in stability to the BA.1 variant. Using dynamic network modeling of the S Omicron BA.1, BA.2, and BA.2.75 trimers, we showed that the key network mediators of allosteric interactions are associated with the major stability hotspots that are interconnected along potential communication pathways. The network analysis of the BA.1, BA.2, and BA.2.75 trimers suggested that the increased thermodynamic stability of the BA.2.75 variant may be linked with the organization and modularity of the residue interaction network that allows for allosteric communications between structural stability hotspots and Omicron mutational sites. This study provided a plausible rationale for a mechanism in which Omicron mutations may evolve by targeting vulnerable sites of conformational adaptability to elicit immune escape while maintaining their control on balancing protein stability and functional fitness through robust allosteric communications with the stability hotspots.

Replica-exchange optimization of antibody fragments

In the framework of the rational design of macromolecules capable of binding to a specific target for biosensing applications, we here further develop an evolutionary protocol designed to optimize the binding affinity of protein binders. In particular we focus on the optimization of the binding portion of small antibody fragments known as nanobodies (or VHH) and choose the hen egg white lysozyme (HEWL) as our target. By implementing a replica exchange scheme for this optimization, we show that an initial hit is not needed and similar solutions can be found by either optimizing an already known anti-HEWL VHH or a randomly selected binder (here a VHH selective towards another macromolecule). While we believe that exhaustive searches of the mutation space are most appropriate when only few key residues have to be optimized, in case a lead binder is not available the proposed evolutionary algorithm should be instead the method of choice.

Deletion of the ATP20 gene in Ustilago maydis produces an unstable dimer of F1FO-ATP synthase associated with a decrease in mitochondrial ATP synthesis and a high H2O2 production

The F₁F_O-ATP synthase uses the energy stored in the electrochemical proton gradient to synthesize ATP. This complex is found in the inner mitochondrial membrane as a monomer and dimer. The dimer shows higher ATPase activity than the monomer and is essential for cristae folding. The monomer-monomer interface is constituted by subunits a, i/j, e, g, and k. The role of the subunit g in a strict respiratory organism is unknown. A gene knockout was generated in Ustilago maydis to study the role of subunit g on mitochondrial metabolism and cristae architecture. Deletion of the ATP20 gene, encoding the g subunit, did not affect cell growth or glucose consumption, but biomass production was lower in the mutant strain (gΔ strain). Ultrastructure observations showed that mitochondrial size and cristae shape were similar in wild-type and gΔ strains. The mitochondrial membrane potential in both strains had a similar magnitude, but oxygen consumption was higher in the WT strain. ATP synthesis was 20 % lower in the gΔ strain. Additionally, the mutant strain expressed the alternative oxidase in the early stages of growth (exponential phase), probably as a response to ROS stress. Dimer from mutant strain was unstable to digitonin solubilization, avoiding its isolation and kinetic characterization. The isolated monomeric state activated by n-dodecyl-β-D-maltopyranoside showed similar kinetic constants to the monomer from the WT strain. A decrease in mitochondrial ATP synthesis and the presence of the AOX during the exponential growth phase suggests that deletion of the g gene induces ROS stress.

Celastrol suppresses humoral immune responses and autoimmunity by targeting the COMMD3/8 complex

Celastrol, a bioactive molecule extracted from the Tripterygium wilfordii plant, has been shown to exhibit anti-inflammatory properties. However, its mechanism of action has not been fully elucidated. Here, we show that celastrol suppresses humoral immune responses and autoimmunity by disabling a protein complex consisting of copper metabolism MURR1 domain–containing (COMMD) 3 and COMMD8 (COMMD3/8 complex), a signaling adaptor for chemoattractant receptors. Having demonstrated the involvement of the COMMD3/8 complex in a mouse model of rheumatoid arthritis, we identified celastrol as a compound that covalently bound to and dissociated the COMMD3/8 complex. Celastrol inhibited B cell migration, reduced antibody responses, and blocked arthritis progression, recapitulating deficiency of the COMMD3/8 complex. These effects of celastrol were abolished in mice expressing a celastrol-resistant mutant of the COMMD3/8 complex. These findings establish that celastrol exerts immunosuppressive activity by targeting the COMMD3/8 complex. Our study suggests that the COMMD3/8 complex is a potentially druggable target in autoimmune diseases and points to celastrol as a lead pharmacologic candidate in this capacity.

3D interaction homology: The hydrophobic residues alanine, isoleucine, leucine, proline and valine play different structural roles in soluble and membrane proteins

The aliphatic hydrophobic amino acid residues—alanine, isoleucine, leucine, proline and valine—are among the most common found in proteins. Their structural role in proteins is seemingly obvious: engage in hydrophobic interactions to stabilize secondary, and to a lesser extent, tertiary and quaternary structure. However, favorable hydrophobic interactions involving the sidechains of these residue types are generally less significant than the unfavorable set arising from interactions with polar atoms. Importantly, the constellation of interactions between residue sidechains and their environments can be recorded as three-dimensional maps that, in turn, can be clustered. The clustered average map sets compose a library of interaction profiles encoding interaction strengths, interaction types and the optimal 3D position for the interacting partners. This library is backbone angle-dependent and suggests solvent and lipid accessibility for each unique interaction profile. In this work, in addition to analysis of soluble proteins, a large set of membrane proteins that contained optimized artificial lipids were evaluated by parsing the structures into three distinct components: soluble extramembrane domain, lipid facing transmembrane domain, core transmembrane domain. The aliphatic residues were extracted from each of these sets and passed through our calculation protocol. Notable observations include: the roles of aliphatic residues in soluble proteins and in the membrane protein’s soluble domains are nearly identical, although the latter are slightly more solvent accessible; by comparing maps calculated with sidechain-lipid interactions to maps ignoring those interactions, the potential extent of residue-lipid and residue-interactions can be assessed and likely exploited in structure prediction and modeling; amongst these residue types, the levels of lipid engagement show isoleucine as the most engaged, while the other residues are largely interacting with neighboring helical residues.

Energy landscapes of Aβ monomers are sculpted in accordance with Ostwald's rule of stages

The transition from a disordered to an assembly-competent monomeric state (N*) in amyloidogenic sequences is a crucial event in the aggregation cascade. Using a well-calibrated model for intrinsically disordered proteins (IDPs), we show that the N* states, which bear considerable resemblance to the polymorphic fibril structures found in experiments, not only appear as excitations in the free energy landscapes of Aβ40 and Aβ42, but also initiate the aggregation cascade. For Aβ42, the transitions to the different N* states are in accord with Ostwald's rule of stages, with the least stable structures forming ahead of thermodynamically favored ones. The Aβ40 and Aβ42 monomer landscapes exhibit different extents of local frustration, which we show have profound implications in dictating subsequent self-assembly. Using kinetic transition networks, we illustrate that the most favored dimerization routes proceed via N* states. We argue that Ostwald's rule also holds for the aggregation of fused in sarcoma and polyglutamine proteins.

Three structural solutions for bacterial adhesion pilus stability and superelasticity

Bacterial adhesion pili are key virulence factors that mediate host-pathogen interactions in diverse epithelial environments. Deploying a multimodal approach, we probed the structural basis underpinning the biophysical properties of pili originating from enterotoxigenic (ETEC) and uropathogenic bacteria. Using cryo-electron microscopy we solved the structures of three vaccine target pili from ETEC bacteria, CFA/I, CS17, and CS20. Pairing these and previous pilus structures with force spectroscopy and steered molecular dynamics simulations, we find a strong correlation between subunit-subunit interaction energies and the force required for pilus unwinding, irrespective of genetic similarity. Pili integrate three structural solutions for stabilizing their assemblies: layer-to-layer interactions, N-terminal interactions to distant subunits, and extended loop interactions from adjacent subunits. Tuning of these structural solutions alters the biophysical properties of pili and promotes the superelastic behavior that is essential for sustained bacterial attachment.

OPUS-Mut: Studying the Effect of Protein Mutation through Side-Chain Modeling

Predicting the effect of protein mutation is crucial in many applications such as protein design, protein evolution, and genetic disease analysis. Structurally, mutation is basically the replacement of the side chain of a particular residue. Therefore, accurate side-chain modeling is useful in studying the effect of mutation. Here, we propose a computational method, namely, OPUS-Mut, which significantly outperforms other backbone-dependent side-chain modeling methods including our previous method OPUS-Rota4. We evaluate OPUS-Mut by four case studies on Myoglobin, p53, HIV-1 protease, and T4 lysozyme. The results show that the predicted structures of side chains of different mutants are consistent well with their experimentally determined results. In addition, when the residues with significant structural shifts upon the mutation are considered, it is found that the extent of the predicted structural shift of these affected residues can be correlated reasonably well with the functional changes of the mutant measured by experiments. OPUS-Mut can also help one to identify the harmful and benign mutations and thus may guide the construction of a protein with relatively low sequence homology but with a similar structure.

Structural and Dynamic Differences between Calreticulin Mutants Associated with Essential Thrombocythemia

Essential thrombocythemia (ET) is a blood cancer. ET is characterized by an overproduction of platelets that can lead to thrombosis formation. Platelet overproduction occurs in megakaryocytes through a signaling pathway that could involve JAK2, MPL, or CALR proteins. CALR mutations are associated with 25–30% of ET patients; CALR variants must be dimerized to induce ET. We classified these variants into five classes named A to E; classes A and B are the most frequent classes in patients with ET. The dynamic properties of these five classes using structural models of CALR’s C-domain were analyzed using molecular dynamics simulations. Classes A, B, and C are associated with frameshifts in the C-domain. Their dimers can be stable only if a disulfide bond is formed; otherwise, the two monomers repulse each other. Classes D and E cannot be stable as dimers due to the absence of disulfide bonds. Class E and wild-type CALR have similar dynamic properties. These results suggest that the disulfide bond newly formed in classes A, B, and C may be essential for the pathogenicity of these variants. They also underline that class E cannot be directly related to ET but corresponds to human polymorphisms.

Intermediate-state-trapped mutants pinpoint G protein-coupled receptor conformational allostery

Understanding the roles of intermediate states in signaling is pivotal to unraveling the activation processes of G protein-coupled receptors (GPCRs). However, the field is still struggling to define these conformational states with sufficient resolution to study their individual functions. Here, we demonstrate the feasibility of enriching the populations of discrete states via conformation-biased mutants. These mutants adopt distinct distributions among five states that lie along the activation pathway of adenosine A2A receptor (A2AR), a class A GPCR. Our study reveals a structurally conserved cation-π lock between transmembrane helix VI (TM6) and Helix8 that regulates cytoplasmic cavity opening as a "gatekeeper" for G protein penetration. A GPCR activation process based on the well-discerned conformational states is thus proposed, allosterically micro-modulated by the cation-π lock and a previously well-defined ionic interaction between TM3 and TM6. Intermediate-state-trapped mutants will also provide useful information in relation to receptor-G protein signal transduction.

Optical control of PIEZO1 channels

PIEZO proteins are unusually large, mechanically-activated trimeric ion channels. The central pore features structural similarities with the pore of other trimeric ion channels, including purinergic P2X receptors, for which optical control of channel gating has been previously achieved with photoswitchable azobenzenes. Extension of these chemical optogenetics methods to mechanically-activated ion channels would provide tools for specific manipulation of pore activity alternative to non-specific mechanical stimulations. Here we report a light-gated mouse PIEZO1 channel, in which an azobenzene-based photoswitch covalently tethered to an engineered cysteine, Y2464C, localized at the extracellular apex of the transmembrane helix 38, rapidly triggers channel gating upon 365-nm-light irradiation. We provide evidence that this light-gated channel recapitulates mechanically-activated PIEZO1 functional properties, and show that light-induced molecular motions are similar to those evoked mechanically. These results push the limits of azobenzene-based methods to unusually large ion channels and provide a simple stimulation means to specifically interrogate PIEZO1 function.

Conserved allosteric inhibition mechanism in SLC1 transporters

Excitatory amino acid transporter 1 (EAAT1) is a glutamate transporter belonging to the SLC1 family of solute carriers. It plays a key role in the regulation of the extracellular glutamate concentration in the mammalian brain. The structure of EAAT1 was determined in complex with UCPH-101, apotent, non-competitive inhibitor of EAAT1. Alanine serine cysteine transporter 2 (ASCT2) is a neutral amino acid transporter, which regulates pools of amino acids such as glutamine between intracellular and extracellular compartments . ASCT2 also belongs to the SLC1 family and shares 58% sequence similarity with EAAT1. However, allosteric modulation of ASCT2 via non-competitive inhibitors is unknown. Here, we explore the UCPH-101 inhibitory mechanisms of EAAT1 and ASCT2 by using rapid kinetic experiments. Our results show that UCPH-101 slows substrate translocation rather than substrate or Na+ binding, confirming a non-competitive inhibitory mechanism, but only partially inhibits wild-type ASCT2. Guided by computational modeling using ligand docking and molecular dynamics simulations, we selected two residues involved in UCPH-101/EAAT1 interaction, which were mutated in ASCT2 (F136Y, I237M, F136Y/I237M) in the corresponding positions. We show that in the F136Y/I237M double-mutant transporter, 100% of the inhibitory effect of UCPH-101 could be restored, and the apparent affinity was increased (Ki = 4.3 μM), much closer to the EAAT1 value of 0.6 μM. Finally, we identify a novel non-competitive ASCT2 inhibitor, through virtual screening and experimental testing against the allosteric site, further supporting its localization. Together, these data indicate that the mechanism of allosteric modulation is conserved between EAAT1 and ASCT2. Due to the difference in binding site residues between ASCT2 and EAAT1, these results raise the possibility that more potent, and potentially selective ASCT2 allosteric inhibitors can be designed .

Systematic analysis and comparison of peptide specificity and selectivity between their cognate receptors and noncognate decoys

Protein-peptide interactions (PpIs) play an important role in cell signaling networks and have been exploited as new and attractive therapeutic targets. The affinity and specificity are two unity-of-opposite aspects of PpIs (and other biomolecular interactions); the former indicates the absolute binding strength between the peptide ligand and its cognate protein receptor in a PpI, while the latter represents the relative recognition selectivity of the peptide ligand for its cognate protein receptor in a PpI over those noncognate decoys that could be potentially encountered by the peptide in cell. Although the PpI binding affinity has been widely investigated over the past decades, the peptide recognition specificity (and selectivity) still remains largely unexplored to date. In this study, we classified PpI specificity into three types: (i) class-I specificity: peptide selectivity for its cognate wild-type protein receptor over the noncognate mutant decoys of this receptor, (ii) class-II specificity: peptide selectivity for its cognate protein receptor over other noncognate decoys that are homologous with this receptor, and (iii) class-III specificity: peptide selectivity for its cognate protein receptor over other noncognate decoys that are the cognate receptors of other peptides. We performed affinity and selectivity analysis for the three types of PpI specificity and revealed that the PpIs generally exhibit a moderate or modest specificity; peptide selectivity increases in the order: class-I < class-II < class-III. All the three types of PpI specificity were observed to have no statistically significant correlation with peptide length and hydrophobicity, but the class-I and class-II specificities can be influenced considerably by peptide secondary structures; the high specificity is preferentially associated with ordered structure types as compared to undefined structure types. In addition, the mutation distribution (for class-I specificity), sequence conservation (for class-II specificity), and structural similarity (for class-III specificity) seem also to address effects on peptide selectivity.

The structure of pathogenic huntingtin exon 1 defines the bases of its aggregation propensity

Huntington's disease is a neurodegenerative disorder caused by a CAG expansion in the first exon of the HTT gene, resulting in an extended polyglutamine (poly-Q) tract in huntingtin (httex1). The structural changes occurring to the poly-Q when increasing its length remain poorly understood due to its intrinsic flexibility and the strong compositional bias. The systematic application of site-specific isotopic labeling has enabled residue-specific NMR investigations of the poly-Q tract of pathogenic httex1 variants with 46 and 66 consecutive glutamines. Integrative data analysis reveals that the poly-Q tract adopts long α-helical conformations propagated and stabilized by glutamine side chain to backbone hydrogen bonds. We show that α-helical stability is a stronger signature in defining aggregation kinetics and the structure of the resulting fibrils than the number of glutamines. Our observations provide a structural perspective of the pathogenicity of expanded httex1 and pave the way to a deeper understanding of poly-Q-related diseases.

GAP positions catalytic H-Ras residue Q61 for GTP hydrolysis in molecular dynamics simulations, complicating chemical rescue of Ras deactivation

Functional interaction of Ras signaling proteins with upstream, negative regulatory GTPase activating proteins (GAPs) represents a crucial step in cellular decision making related to growth and survival. Key components of the catalytic transition state for Ras deactivation by GAP-accelerated hydrolysis of Ras-bound guanosine triphosphate (GTP) are thought to include an arginine residue from the GAP (the arginine finger), a glutamine residue from Ras (Q61), and a water molecule that is likely coordinated by Q61 to engage in nucleophilic attack on GTP. Here, we use in-vitro fluorescence experiments to show that 0.1-100 mM concentrations of free arginine, imidazole, and other small nitrogenous molecule fail to accelerate GTP hydrolysis, even in the presence of the catalytic domain of a mutant GAP lacking its arginine finger (R1276A NF1). This result is surprising given that imidazole can chemically rescue enzyme activity in arginine-to-alanine mutant protein tyrosine kinases (PTKs) that share many active site components with Ras/GAP complexes. Complementary all-atom molecular dynamics (MD) simulations reveal that an arginine finger GAP mutant still functions to enhance Ras Q61-GTP interaction, though less extensively than wild-type GAP. This increased Q61-GTP proximity may promote more frequent fluctuations into configurations that enable GTP hydrolysis as a component of the mechanism by which GAPs accelerate Ras deactivation in the face of arginine finger mutations. The failure of small molecule analogs of arginine to chemically rescue catalytic deactivation of Ras is consistent with the idea that the influence of the GAP goes beyond the simple provision of its arginine finger. However, the failure of chemical rescue in the presence of R1276A NF1 suggests that the GAPs arginine finger is either unsusceptible to rescue due to exquisite positioning or that it is involved in complex multivalent interactions. Therefore, in the context of oncogenic Ras proteins with mutations at codons 12 or 13 that inhibit arginine finger penetration toward GTP, drug-based chemical rescue of GTP hydrolysis may have bifunctional chemical/geometric requirements that are more difficult to satisfy than those that result from arginine-to-alanine mutations in other enzymes for which chemical rescue has been demonstrated.

Alternative Strategy for Spectral Tuning of Flavin-Binding Fluorescent Proteins

iLOV is an engineered flavin-binding fluorescent protein (FbFP) with applications for in vivo cellular imaging. To expand the range of applications of FbFPs for multicolor imaging and FRET-based biosensing, it is desirable to understand how to modify their absorption and emission wavelengths (i.e., through spectral tuning). There is particular interest in developing FbFPs that absorb and emit light at longer wavelengths, which has proven challenging thus far. Existing spectral tuning strategies that do not involve chemical modification of the flavin cofactor have focused on placing positively charged amino acids near flavin's C4a and N5 atoms. Guided by previously reported electrostatic spectral tunning maps (ESTMs) of the flavin cofactor and by quantum mechanical/molecular mechanical (QM/MM) calculations reported in this work, we suggest an alternative strategy: placing a negatively charged amino acid near flavin's N1 atom. We predict that a single-point mutant, iLOV-Q430E, has a slightly red-shifted absorption and fluorescence maximum wavelength relative to iLOV. To validate our theoretical prediction, we experimentally expressed and purified iLOV-Q430E and measured its spectral properties. We found that the Q430E mutation results in a slight change in absorption and a 4-8 nm red shift in the fluorescence relative to iLOV, in good agreement with the computational predictions. Molecular dynamics simulations showed that the carboxylate side chain of the glutamate in iLOV-Q430E points away from the flavin cofactor, which leads to a future expectation that further red shifting may be achieved by bringing the side chain closer to the cofactor.

Engineering the kinetic stability of a β-trefoil protein by tuning its topological complexity

Kinetic stability, defined as the rate of protein unfolding, is central to determining the functional lifetime of proteins, both in nature and in wide-ranging medical and biotechnological applications. Further, high kinetic stability is generally correlated with high resistance against chemical and thermal denaturation, as well as proteolytic degradation. Despite its significance, specific mechanisms governing kinetic stability remain largely unknown, and few studies address the rational design of kinetic stability. Here, we describe a method for designing protein kinetic stability that uses protein long-range order, absolute contact order, and simulated free energy barriers of unfolding to quantitatively analyze and predict unfolding kinetics. We analyze two β-trefoil proteins: hisactophilin, a quasi-three-fold symmetric natural protein with moderate stability, and ThreeFoil, a designed three-fold symmetric protein with extremely high kinetic stability. The quantitative analysis identifies marked differences in long-range interactions across the protein hydrophobic cores that partially account for the differences in kinetic stability. Swapping the core interactions of ThreeFoil into hisactophilin increases kinetic stability with close agreement between predicted and experimentally measured unfolding rates. These results demonstrate the predictive power of readily applied measures of protein topology for altering kinetic stability and recommend core engineering as a tractable target for rationally designing kinetic stability that may be widely applicable.

Design of a protein-targeted DNA aptamer using atomistic simulation

The concentrations of specific macromolecular species can be quantified using diagnostic tools that rely on molecular recognition by nucleic acid aptamers. One such approach involves the formation of osmium tetroxide 2,2'-bipyridine protein adducts, followed by electrochemical detection of analytes that bind specifically to electrode-tethered aptamers. In conjunction with a 27-mer DNA aptamer that binds specifically to exosite II on human alpha thrombin, this technique permits, in theory, a highly sensitive diagnostic tool for the quantification of serum thrombin levels. However, thrombin's aptamer binding site is lined by two tryptophan residues and the conjugation of bulky osmium groups to these residues weakens aptamer binding by an estimated 4 to 12 kcal/mol, undermining detection sensitivity. Therefore, we have rationally modified this DNA aptamer to strengthen its thrombin binding in the presence of conjugated osmium. Specifically, aptamers carrying long hydrophobic thymine derivatives in place of guanine 21 have binding affinities for osmium-conjugated thrombin that are enhanced by 10 to 15 kcal/mol, suggesting that these modified aptamers may be effective in a highly sensitive electrochemical sensor for the quantification of low concentrations of thrombin. Our approach of using molecular simulation to subtly re-engineer a DNA aptamer may be generally applicable for the optimization of other macromolecular binding interfaces.Communicated by Ramaswamy H. Sarma.

Self-recycling and partially conservative replication of mycobacterial methylmannose polysaccharides

The steep increase in nontuberculous mycobacteria (NTM) infections makes understanding their unique physiology an urgent health priority. NTM synthesize two polysaccharides proposed to modulate fatty acid metabolism: the ubiquitous 6-O-methylglucose lipopolysaccharide, and the 3-O-methylmannose polysaccharide (MMP) so far detected in rapidly growing mycobacteria. The recent identification of a unique MMP methyltransferase implicated the adjacent genes in MMP biosynthesis. We report a wide distribution of this gene cluster in NTM, including slowly growing mycobacteria such as Mycobacterium avium, which we reveal to produce MMP. Using a combination of MMP purification and chemoenzymatic syntheses of intermediates, we identified the biosynthetic mechanism of MMP, relying on two enzymes that we characterized biochemically and structurally: a previously undescribed α-endomannosidase that hydrolyses MMP into defined-sized mannoligosaccharides that prime the elongation of new daughter MMP chains by a rare α-(1→4)-mannosyltransferase. Therefore, MMP biogenesis occurs through a partially conservative replication mechanism, whose disruption affected mycobacterial growth rate at low temperature.

Comparative Surface Electrostatics and Normal Mode Analysis of High and Low Pathogenic H7N7 Avian Influenza Viruses

Influenza A viruses are rarely symptomatic in wild birds, while representing a higher threat to poultry and mammals, where they can cause a variety of symptoms, including death. H5 and H7 subtypes of influenza viruses are of particular interest because of their pathogenic potential and reported capacity to spread from poultry to mammals, including humans. The identification of molecular fingerprints for pathogenicity can help surveillance and early warning systems, which are crucial to prevention and protection from such potentially pandemic agents. In the past decade, comparative analysis of the surface features of hemagglutinin, the main protein antigen in influenza viruses, identified electrostatic fingerprints in the evolution and spreading of H5 and H9 subtypes. Electrostatic variation among viruses from avian or mammalian hosts was also associated with host jump. Recent findings of fingerprints associated with low and highly pathogenic H5N1 viruses, obtained by means of comparative electrostatics and normal modes analysis, prompted us to check whether such fingerprints can also be found in the H7 subtype. Indeed, evidence presented in this work showed that also in H7N7, hemagglutinin proteins from low and highly pathogenic strains present differences in surface electrostatics, while no meaningful variation was found in normal modes.

Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein-ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein-ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein-ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein-ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein-ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein-ligand interactions on cryo-EM data using prior structural information.

Thermodynamic analysis of the GASright transmembrane motif supports energetic model of dimerization

The GASright motif, best known as the fold of the glycophorin A transmembrane dimer, is one of the most common dimerization motifs in membrane proteins, characterized by its hallmark GxxxG-like sequence motifs (GxxxG, AxxxG, GxxxS, and similar). Structurally, GASright displays a right-handed crossing angle and short interhelical distance. Contact between the helical backbones favors the formation of networks of weak hydrogen bonds between Cα-H carbon donors and carbonyl acceptors on opposing helices (Cα-H···O=C). To understand the factors that modulate the stability of GASright, we previously presented a computational and experimental structure-based analysis of 26 predicted dimers. We found that the contributions of van der Waals packing and Cα-H hydrogen bonding to stability, as inferred from the structural models, correlated well with relative dimerization propensities estimated experimentally with the in vivo assay TOXCAT. Here we test this model with a quantitative thermodynamic analysis. We used Förster resonance energy transfer (FRET) to determine the free energy of dimerization of a representative subset of seven of the 26 original TOXCAT dimers using FRET. To overcome the technical issue arising from limited sampling of the dimerization isotherm, we introduced a globally fitting strategy across a set of constructs comprising a wide range of stabilities. This strategy yielded precise thermodynamic data that show strikingly good agreement between the original propensities and ΔG° of association in detergent, suggesting that TOXCAT is a thermodynamically driven process. From the correlation between TOXCAT and thermodynamic stability, the predicted free energy for all the 26 GASright dimers was calculated. These energies correlate with the in silico ΔE scores of dimerization that were computed on the basis of their predicted structure. These findings corroborate our original model with quantitative thermodynamic evidence, strengthening the hypothesis that van der Waals and Cα-H hydrogen bond interactions are the key modulators of GASright stability.

Structural Modeling of Adaptive Immune Responses to Infection

Antibody and TCR modeling are becoming important as more and more sequence data becomes available to the public. One of the pressing questions now is how to use such data to understand adaptive immune responses to disease. Infectious disease is of particular interest because the antigens driving such responses are often known to some extent. Here, we describe tips for gathering data and cleaning it for use in downstream analysis. We present a method for high-throughput structural modeling of antibodies or TCRs using Repertoire Builder and its extensions. AbAdapt is an extension of Repertoire Builder for antibody-antigen docking from antibody and antigen sequences. ImmuneScape is a corresponding extension for TCR-pMHC 3D modeling. Together, these pipelines can help researchers to understand immune responses to infection from a structural point of view.

Engineering human JMJD2A tudor domains for an improved understanding of histone peptide recognition

JMJD2A is a histone lysine demethylase which recognizes and demethylates H3K9me3 and H3K36me3 residues and is overexpressed in various cancers. It utilizes a tandem tudor domain to facilitate its own recruitment to histone sites, recognizing various di- and tri-methyl lysine residues with moderate affinity. In this study, we successfully engineered the tudor domain of JMJD2A to specifically bind to H4K20me3 with a 20-fold increase of affinity and improved selectivity. To reveal the molecular basis, we performed molecular dynamics and free energy decomposition analysis on the human JMJD2A tandem tudor domains bound to H4K20me2, H4K20me3, and H3K23me3 peptides to uncover the residues and conformational changes important for the enhanced binding affinity and selectivity toward H4K20me2/3. These analyses revealed new insights into understanding chromatin reader domains recognizing histone modifications and improving binding affinity and selectivity of these domains. Furthermore, we showed that the tight binding of JMJD2A to H4K20me2/3 is not sufficient to improve the efficiency of CRISPR-CAS9 mediated homology directed repair (HDR), suggesting a complicated relationship between JMJD2A and the DNA damage response beyond binding affinity toward the H4K20me2/3 mark.

3D-BMPP: 3D Beta-Barrel Membrane Protein Predictor

β-barrel membrane proteins (βMPs), found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts, play important roles in membrane anchoring, pore formation, and enzyme activities. However, it is often difficult to determine their structures experimentally, and the knowledge of their structures is currently limited. We have developed a method to predict the 3D architectures of βMPs. We can accurately construct transmembrane domains of βMPs by predicting their strand registers, from which full 3D atomic structures are derived. Using 3D Beta-barrel Membrane Protein Predictor (3D-BMPP), we can further accurately model the extended beta barrels and loops in non-TM regions with overall greater structure prediction coverage. 3DBMPP is a general technique that can be applied to protein families with limited sequences as well as proteins with novel folds. Applications of 3DBMPP can be broadly applied to genome-wide βMPs structure prediction.

Two new Scianna variants causing loss of high prevalence antigens: ERMAP model and 3D analysis of the antigens

BACKGROUND

Scianna (Sc) antigens, seven high and two of low prevalence, are expressed on erythrocyte membrane-associated protein (ERMAP). We investigated SC (ERMAP) in individuals who made antibodies to high prevalence Scianna antigens, and propose a 3D model for ERMAP to precisely localize the residues associated with the known antigens.

METHODS

Serological testing and DNA sequencing was performed by standard methods. A 3D structural model was built using a multi-template homology approach. Protein structures representing missense variants associated with the loss or gain of an antigen were generated. Residue accessibility and intraprotein interactions were compared with the wild-type protein.

RESULTS

Two new SC alleles, one with c.349C > T (p.Arg117Cys) in a woman from South India with anti-Sc3 in her plasma, and a c.217_219delinsTGT (p.Arg73Cys) in an African-American woman with an antibody to a new high prevalence antigen, termed SCAC, were identified. Six structural templates were used to model ERMAP. 3D analysis showed that residues key for Scianna antigen expression were all exposed at the surface of the extracellular domain. The p.Arg117Cys change was predicted to abolish interactions between residues 93 and 117, with no compensating interactions.

CONCLUSION

We confirm the extracellular location of Scianna residues responsible for antigen expression which predicts direct accessibility to antibodies. Loss of intraprotein interactions appear to be responsible for a Sc null and production of anti-Sc3 with p.117Cys, SC*01 N.03, and for loss of a high prevalence antigen with p.73Cys, termed SCAC for Sc Arg to Cys. Comparative modeling aids our understanding of new alleles and Scianna antigen expression.

Homology Modeling of Antibody Variable Regions: Methods and Applications

Adaptive immunity specifically protects us from antigenic challenges. Antibodies are key effector proteins of adaptive immunity, and they are remarkable in their ability to recognize a virtually limitless number of antigens. Fragment variable (F_V), the antigen-binding region of antibodies, can be split into two main components, namely, framework and complementarity determining regions. The framework (FR) consists of light-chain framework (FR_L) and heavy-chain framework (FR_H). Similarly, the complementarity determining regions (CDRs) comprises of light-chain CDRs 1-3 (CDRs L1-3) and heavy-chain CDRs 1-3 (CDRs H1-3). While FRs are relatively constant in sequence and structure across diverse antibodies, sequence variation in CDRs leading to differential conformations of CDR loops accounts for the distinct antigenic specificities of diverse antibodies. The conserved structural features in FRs and conformity of CDRs to a limited set of standard conformations allow for the accurate prediction of F_V models using homology modeling techniques. Antibody structure prediction from its amino acid sequence has numerous important applications including prediction of antibody-antigen interaction interfaces and redesign of therapeutically and biotechnologically useful antibodies with improved affinity. This chapter summarizes the current practices employed in the successful homology modeling of antibody variable regions and the potential applications of the generated homology models.

Antibody Affinity Maturation Using Computational Methods: From an Initial Hit to Small-Scale Expression of Optimized Binders

Nanobodies (VHHs) are engineered fragments of the camelid single-chain immunoglobulins. The VHH domain contains the highly variable segments responsible for antigen recognition. VHHs can be easily produced as recombinant proteins. Their small size is a good advantage for in silico approaches. Computer methods represent a valuable strategy for the optimization and improvement of their binding affinity. They also allow for epitope selection offering the possibility to design new VHHs for regions of a target protein that are not naturally immunogenic. Here we present an in silico mutagenic protocol developed to improve the binding affinity of nanobodies together with the first step of their in vitro production. The method, already proven successful in improving the low Kd of a nanobody hit obtained by panning, can be employed for the ex novo design of antibody fragments against selected protein target epitopes.

Optimizing Antibody-Antigen Binding Affinities with the ADAPT Platform

The ADAPT (Assisted Design of Antibody and Protein Therapeutics) platform guides the selection of mutants that improve/modulate the affinity of antibodies and other biologics. Predicted affinities are based on a consensus z-score from three scoring functions. Computational predictions are interleaved with experimental validation, significantly enhancing the robustness of the design and selection of mutants. A key step is an initial exhaustive virtual single-mutant scan that identifies hot spots and the mutations predicted to improve affinity. A small number of proposed single mutants are then produced and assayed. Only the validated single mutants (i.e., having improved affinity) are used to design double and higher-order mutants in subsequent rounds of design, avoiding the combinatorial explosion that arises from random mutagenesis. Typically, with a total of about 30-50 designed single, double, and triple mutants, affinity improvements of 10- to 100-fold are obtained.

Remdesivir analogs against SARS-CoV-2 RNA-dependent RNA polymerase

The COVID-19 pandemic has already taken many lives but is still continuing its spread and exerting jeopardizing effects. This study is aimed to find the most potent ligands from 703 analogs of remdesivir against RNA-dependent RNA polymerase (RdRp) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus . RdRp is a major part of a multi-subunit transcription complex of the virus, which is essential for viral replication. In clinical trials, it has been found that remdesivir is effective to inhibit viral replication in Ebola and in primary human lung cell cultures; it effectively impedes replication of a broad-spectrum pre-pandemic bat coronaviruses and epidemic human coronaviruses. After virtual screening, 30 most potent ligands and remdesivir were modified with triphosphate. Quantum mechanics-based quantitative structure-activity relationship envisages the binding energy for ligands applying partial least square (PLS) regression. PLS regression remarkably predicts the binding energy of the effective ligands with an accuracy of 80% compared to the value attained from molecular docking. Two ligands (L4:58059550 and L28:126719083), which have more interactions with the target protein than the other ligands including standard remdesivir triphosphate, were selected for further analysis. Molecular dynamics simulation is done to assess the stability and dynamic nature of the drug-protein complex. Binding-free energy results via PRODIGY server and molecular mechanics/Poisson-Boltzmann surface area method depict that the potential and solvation energies play a crucial role. Considering all computational analysis, we recommend the best remdesivir analogs can be utilized for efficacy test through in vitro and in vivo trials against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Computational analysis of protein stability and allosteric interaction networks in distinct conformational forms of the SARS-CoV-2 spike D614G mutant: reconciling functional mechanisms through allosteric model of spike regulation

In this study, we used an integrative computational approach to examine molecular mechanisms underlying functional effects of the D614G mutation by exploring atomistic modeling of the SARS-CoV-2 spike proteins as allosteric regulatory machines. We combined coarse-grained simulations, protein stability and dynamic fluctuation communication analysis with network-based community analysis to examine structures of the native and mutant SARS-CoV-2 spike proteins in different functional states. Through distance fluctuations communication analysis, we probed stability and allosteric communication propensities of protein residues in the native and mutant SARS-CoV-2 spike proteins, providing evidence that the D614G mutation can enhance long-range signaling of the allosteric spike engine. By combining functional dynamics analysis and ensemble-based alanine scanning of the SARS-CoV-2 spike proteins we found that the D614G mutation can improve stability of the spike protein in both closed and open forms, but shifting thermodynamic preferences towards the open mutant form. Our results revealed that the D614G mutation can promote the increased number of stable communities and allosteric hub centers in the open form by reorganizing and enhancing the stability of the S1-S2 inter-domain interactions and restricting mobility of the S1 regions. This study provides atomistic-based view of allosteric communications in the SARS-CoV-2 spike proteins, suggesting that the D614G mutation can exert its primary effect through allosterically induced changes on stability and communications in the residue interaction networks.Communicated by Ramaswamy H. Sarma.

Structure and Dynamics of the Unassembled Nucleoprotein of Rabies Virus in Complex with Its Phosphoprotein Chaperone Module

As for all non-segmented negative RNA viruses, rabies virus has its genome packaged in a linear assembly of nucleoprotein (N), named nucleocapsid. The formation of new nucleocapsids during virus replication in cells requires the production of soluble N protein in complex with its phosphoprotein (P) chaperone. In this study, we reconstituted a soluble heterodimeric complex between an armless N protein of rabies virus (RABV), lacking its N-terminal subdomain (N_NT-ARM), and a peptide encompassing the N⁰ chaperon module of the P protein. We showed that the chaperone module undergoes a disordered-order transition when it assembles with N⁰ and measured an affinity in the low nanomolar range using a competition assay. We solved the crystal structure of the complex at a resolution of 2.3 Å, unveiling the details of the conserved interfaces. MD simulations showed that both the chaperon module of P and RNA-mediated polymerization reduced the ability of the RNA binding cavity to open and close. Finally, by reconstituting a complex with full-length P protein, we demonstrated that each P dimer could independently chaperon two N⁰ molecules.

A backbone-dependent rotamer library with high (ϕ, ψ) coverage using metadynamics simulations

Backbone-dependent rotamer libraries are commonly used to assign the side chain dihedral angles of amino acids when modeling protein structures. Most rotamer libraries are created by curating protein crystal structure data and using various methods to extrapolate the existing data to cover all possible backbone conformations. However, these rotamer libraries may not be suitable for modeling the structures of cyclic peptides and other constrained peptides because these molecules frequently sample backbone conformations rarely seen in the crystal structures of linear proteins. To provide backbone-dependent side chain information beyond the α-helix, β-sheet, and PPII regions, we used explicit-solvent metadynamics simulations of model dipeptides to create a new rotamer library that has high coverage in the (ϕ, ψ) space. Furthermore, this approach can be applied to build high-coverage rotamer libraries for noncanonical amino acids. The resulting Metadynamics of Dipeptides for Rotamer Distribution (MEDFORD) rotamer library predicts the side chain conformations of high-resolution protein crystal structures with similar accuracy (~80%) to a state-of-the-art rotamer library. Our ability to test the accuracy of MEDFORD at predicting the side chain dihedral angles of amino acids in noncanonical backbone conformation is restricted by the limited structural data available for cyclic peptides. For the cyclic peptide data that are currently available, MEDFORD and the state-of-the-art rotamer library perform comparably. However, the two rotamer libraries indeed make different rotamer predictions in noncanonical (ϕ, ψ) regions. For noncanonical amino acids, the MEDFORD rotamer library predicts the χ1 values with approximately 75% accuracy.

GeoPacker: A novel deep learning framework for protein side-chain modeling

Atomic interactions play essential roles in protein folding, structure stabilization, and function performance. Recent advances in deep learning-based methods have achieved impressive success not only in protein structure prediction, but also in protein sequence design. However, highly efficient and accurate protein side-chain prediction methods that can give detailed atomic interactions are still lacking. In the present study, we developed a deep learning based method, GeoPacker, that uses geometric deep learning coupled ResNet for protein side-chain modeling. GeoPacker explicitly represents atomic interactions with rotational and translational invariance for information extraction of relative locations. GeoPacker outperformed the state-of-the-art energy function-based methods in side-chain structure prediction accuracy and runs about 10 and 700 times faster than the deep learning-based method DLPacker and OPUS-rota4 with comparable prediction accuracy, respectively. The performance of GeoPacker does not depend on the secondary structures that the residues belong to. GeoPacker gives highly accurate predictions for buried residues in the protein core as well as protein-protein interface, making it a useful tool for protein structure modeling, protein, and interaction design.