Macromolecular modeling and design in Rosetta: recent methods and frameworks

Reconfigurable asymmetric protein assemblies through implicit negative design

Asymmetric multiprotein complexes that undergo subunit exchange play central roles in biology but present a challenge for design because the components must not only contain interfaces that enable reversible association but also be stable and well behaved in isolation. We use implicit negative design to generate β sheet-mediated heterodimers that can be assembled into a wide variety of complexes. The designs are stable, folded, and soluble in isolation and rapidly assemble upon mixing, and crystal structures are close to the computational models. We construct linearly arranged hetero-oligomers with up to six different components, branched hetero-oligomers, closed C4-symmetric two-component rings, and hetero-oligomers assembled on a cyclic homo-oligomeric central hub and demonstrate that such complexes can readily reconfigure through subunit exchange. Our approach provides a general route to designing asymmetric reconfigurable protein systems.

Spike-Dependent Opsonization Indicates Both Dose-Dependent Inhibition of Phagocytosis and That Non-Neutralizing Antibodies Can Confer Protection to SARS-CoV-2

Spike-specific antibodies are central to effective COVID19 immunity. Research efforts have focused on antibodies that neutralize the ACE2-Spike interaction but not on non-neutralizing antibodies. Antibody-dependent phagocytosis is an immune mechanism enhanced by opsonization, where typically, more bound antibodies trigger a stronger phagocyte response. Here, we show that Spike-specific antibodies, dependent on concentration, can either enhance or reduce Spike-bead phagocytosis by monocytes independently of the antibody neutralization potential. Surprisingly, we find that both convalescent patient plasma and patient-derived monoclonal antibodies lead to maximum opsonization already at low levels of bound antibodies and is reduced as antibody binding to Spike protein increases. Moreover, we show that this Spike-dependent modulation of opsonization correlate with the outcome in an experimental SARS-CoV-2 infection model. These results suggest that the levels of anti-Spike antibodies could influence monocyte-mediated immune functions and propose that non-neutralizing antibodies could confer protection to SARS-CoV-2 infection by mediating phagocytosis.

Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation

Understanding and predicting the functional consequences of single amino acid changes is central in many areas of protein science. Here, we collect and analyze experimental measurements of effects of >150,000 variants in 29 proteins. We use biophysical calculations to predict changes in stability for each variant and assess them in light of sequence conservation. We find that the sequence analyses give more accurate prediction of variant effects than predictions of stability and that about half of the variants that show loss of function do so due to stability effects. We construct a machine learning model to predict variant effects from protein structure and sequence alignments and show how the two sources of information support one another and enable mechanistic interpretations. Together, our results show how one can leverage large-scale experimental assessments of variant effects to gain deeper and general insights into the mechanisms that cause loss of function.

A flexible GAS belt responds to pore mutations changing the ion selectivity of proton-gated channels

Proton-gated ion channels conduct mainly Na+ to induce postsynaptic membrane depolarization. Finding the determinants of ion selectivity requires knowledge of the pore structure in the open conformation, but such information is not yet available. Here, the open conformation of the hASIC1a channel was computationally modeled, and functional effects of pore mutations were analyzed in light of the predicted structures. The open pore structure shows two constrictions of similar diameter formed by the backbone of the GAS belt and, right beneath it, by the side chains of H28 from the reentrant loop. Models of nonselective mutant channels, but not those that maintain ion selectivity, predict enlargement of the GAS belt, suggesting that this motif is quite flexible and that the loss of stabilizing interactions in the central pore leads to changes in size/shape of the belt. Our results are consistent with the "close-fit" mechanism governing selectivity of hASIC1a, wherein the backbone of the GAS substitutes at least part of the hydration shell of a permeant ion to enable crossing the pore constriction.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

Applications of machine learning in computer-aided drug discovery

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

Agonist antibody discovery: Experimental, computational, and rational engineering approaches

Agonist antibodies that activate cellular signaling have emerged as promising therapeutics for treating myriad pathologies. Unfortunately, the discovery of rare antibodies with the desired agonist functions is a major bottleneck during drug development. Nevertheless, there has been important recent progress in discovering and optimizing agonist antibodies against a variety of therapeutic targets that are activated by diverse signaling mechanisms. Herein, we review emerging high-throughput experimental and computational methods for agonist antibody discovery as well as rational molecular engineering methods for optimizing their agonist activity.

Structure of Hsp90-Hsp70-Hop-GR reveals the Hsp90 client-loading mechanism

Maintaining a healthy proteome is fundamental for the survival of all organisms¹. Integral to this are Hsp90 and Hsp70, molecular chaperones that together facilitate the folding, remodelling and maturation of the many 'client proteins' of Hsp90². The glucocorticoid receptor (GR) is a model client protein that is strictly dependent on Hsp90 and Hsp70 for activity^3-7. Chaperoning GR involves a cycle of inactivation by Hsp70; formation of an inactive GR-Hsp90-Hsp70-Hop 'loading' complex; conversion to an active GR-Hsp90-p23 'maturation' complex; and subsequent GR release⁸. However, to our knowledge, a molecular understanding of this intricate chaperone cycle is lacking for any client protein. Here we report the cryo-electron microscopy structure of the GR-loading complex, in which Hsp70 loads GR onto Hsp90, uncovering the molecular basis of direct coordination by Hsp90 and Hsp70. The structure reveals two Hsp70 proteins, one of which delivers GR and the other scaffolds the Hop cochaperone. Hop interacts with all components of the complex, including GR, and poises Hsp90 for subsequent ATP hydrolysis. GR is partially unfolded and recognized through an extended binding pocket composed of Hsp90, Hsp70 and Hop, revealing the mechanism of GR loading and inactivation. Together with the GR-maturation complex structure⁹, we present a complete molecular mechanism of chaperone-dependent client remodelling, and establish general principles of client recognition, inhibition, transfer and activation.

Learning Strategies in Protein Directed Evolution

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.

The Zip4 protein directly couples meiotic crossover formation to synaptonemal complex assembly

Meiotic recombination is triggered by programmed double-strand breaks (DSBs), a subset of these being repaired as crossovers, promoted by eight evolutionarily conserved proteins, named ZMM. Crossover formation is functionally linked to synaptonemal complex (SC) assembly between homologous chromosomes, but the underlying mechanism is unknown. Here we show that Ecm11, a SC central element protein, localizes on both DSB sites and sites that attach chromatin loops to the chromosome axis, which are the starting points of SC formation, in a way that strictly requires the ZMM protein Zip4. Furthermore, Zip4 directly interacts with Ecm11, and point mutants that specifically abolish this interaction lose Ecm11 binding to chromosomes and exhibit defective SC assembly. This can be partially rescued by artificially tethering interaction-defective Ecm11 to Zip4. Mechanistically, this direct connection ensuring SC assembly from CO sites could be a way for the meiotic cell to shut down further DSB formation once enough recombination sites have been selected for crossovers, thereby preventing excess crossovers. Finally, the mammalian ortholog of Zip4, TEX11, also interacts with the SC central element TEX12, suggesting a general mechanism.

Computational design of an apoptogenic protein that binds BCL-xL and MCL-1 simultaneously and potently

One of the hallmarks of cancer cells is their ability to evade apoptosis, which confers survival advantages and resistance to anti-cancer drugs. Cancers often exhibit overexpression of anti-apoptotic BCL-2 proteins, the loss of which triggers apoptosis. In particular, the inhibition of both BCL-xL and MCL-1, but neither one individually, synergistically enhances apoptotic cell death. Here, we report computational design to produce a protein that inhibits both BCL-xL and MCL-1 simultaneously. To a reported artificial three-helix bundle whose second helix was designed to bind MCL-1, we added a fourth helix and designed it to bind BCL-xL. After structural validation of the design and further structure-based sequence design, we produced a dual-binding protein that interacts with both BCL-xL and MCL-1 with apparent dissociation constants of 820 pM and 196 pM, respectively. Expression of this dual binder in a subset of cancer cells induced apoptotic cell death at levels significantly higher than those induced by the pro-apoptotic BIM protein. With a genetic fusion of a mitochondria-targeting sequence or the BH3 sequence of BIM, the activity of the dual binder was enhanced even further. These data suggest that targeted delivery of this dual binder alone or as a part of a modular protein to cancers in the form of protein, mRNA, or DNA may be an effective way to induce cancer cell apoptosis.

Computational design of peptides to target NaV1.7 channel with high potency and selectivity for the treatment of pain

The voltage-gated sodium Na_V1.7 channel plays a key role as a mediator of action potential propagation in C-fiber nociceptors and is an established molecular target for pain therapy. ProTx-II is a potent and moderately selective peptide toxin from tarantula venom that inhibits human Na_V1.7 activation. Here we used available structural and experimental data to guide Rosetta design of potent and selective ProTx-II-based peptide inhibitors of human Na_V1.7 channels. Functional testing of designed peptides using electrophysiology identified the PTx2-3127 and PTx2-3258 peptides with IC₅₀s of 7 nM and 4 nM for hNa_V1.7 and more than 1000-fold selectivity over human Na_V1.1, Na_V1.3, Na_V1.4, Na_V1.5, Na_V1.8, and Na_V1.9 channels. PTx2-3127 inhibits Na_V1.7 currents in mouse and human sensory neurons and shows efficacy in rat models of chronic and thermal pain when administered intrathecally. Rationally designed peptide inhibitors of human Na_V1.7 channels have transformative potential to define a new class of biologics to treat pain.

Computational design of a neutralizing antibody with picomolar binding affinity for all concerning SARS-CoV-2 variants

Coronavirus disease 2019, caused by SARS-CoV-2, remains an on-going pandemic, partly due to the emergence of variant viruses that can "break-through" the protection of the current vaccines and neutralizing antibodies (nAbs), highlighting the needs for broadly nAbs and next-generation vaccines. We report an antibody that exhibits breadth and potency in binding the receptor-binding domain (RBD) of the virus spike glycoprotein across SARS coronaviruses. Initially, a lead antibody was computationally discovered and crystallographically validated that binds to a highly conserved surface of the RBD of wild-type SARS-CoV-2. Subsequently, through experimental affinity enhancement and computational affinity maturation, it was further developed to bind the RBD of all concerning SARS-CoV-2 variants, SARS-CoV-1 and pangolin coronavirus with pico-molar binding affinities, consistently exhibited strong neutralization activity against wild-type SARS-CoV-2 and the Alpha and Delta variants. These results identify a vulnerable target site on coronaviruses for development of pan-sarbecovirus nAbs and vaccines.

Single amino acid residue mediates reciprocal specificity in two mosquito odorant receptors

The southern house mosquito, Culex quinquefasciatus, utilizes two odorant receptors, CquiOR10 and CquiOR2, narrowly tuned to oviposition attractants and well conserved among mosquito species. They detect skatole and indole, respectively, with reciprocal specificity. We swapped the transmembrane (TM) domains of CquiOR10 and CquiOR2 and identified TM2 as a specificity determinant. With additional mutations, we showed that CquiOR10A73L behaved like CquiOR2. Conversely, CquiOR2L74A recapitulated CquiOR10 specificity. Next, we generated structural models of CquiOR10 and CquiOR10A73L using RoseTTAFold and AlphaFold and docked skatole and indole using RosettaLigand. These modeling studies suggested space-filling constraints around A73. Consistent with this hypothesis, CquiOR10 mutants with a bulkier residue (Ile, Val) were insensitive to skatole and indole, whereas CquiOR10A73G retained the specificity to skatole and showed a more robust response than the wildtype receptor CquiOR10. On the other hand, Leu to Gly mutation of the indole receptor CquiOR2 reverted the specificity to skatole. Lastly, CquiOR10A73L, CquiOR2, and CquiOR2L74I were insensitive to 3-ethylindole, whereas CquiOR2L74A and CquiOR2L74G gained activity. Additionally, CquiOR10A73G gave more robust responses to 3-ethylindole than CquiOR10. Thus, we suggest the specificity of these receptors is mediated by a single amino acid substitution, leading to finely tuned volumetric space to accommodate specific oviposition attractants.

Deep generative modeling for protein design

Deep learning approaches have produced substantial breakthroughs in fields such as image classification and natural language processing and are making rapid inroads in the area of protein design. Many generative models of proteins have been developed that encompass all known protein sequences, model specific protein families, or extrapolate the dynamics of individual proteins. Those generative models can learn protein representations that are often more informative of protein structure and function than hand-engineered features. Furthermore, they can be used to quickly propose millions of novel proteins that resemble the native counterparts in terms of expression level, stability, or other attributes. The protein design process can further be guided by discriminative oracles to select candidates with the highest probability of having the desired properties. In this review, we discuss five classes of generative models that have been most successful at modeling proteins and provide a framework for model guided protein design.

Cryo-EM structure of the autoinhibited state of myosin-2

We solved the near-atomic resolution structure of smooth muscle myosin-2 in the autoinhibited state (10S) using single-particle cryo–electron microscopy. The 3.4-Å structure reveals the precise molecular architecture of 10S and the structural basis for myosin-2 regulation. We reveal the position of the phosphorylation sites that control myosin autoinhibition and activation by phosphorylation of the regulatory light chain. Further, we present a previously unidentified conformational state in myosin-2 that traps ADP and Pi produced by the hydrolysis of ATP in the active site. This noncanonical state represents a branch of the myosin enzyme cycle and explains the autoinhibition of the enzyme function of 10S along with its reduced affinity for actin. Together, our structure defines the molecular mechanisms that drive 10S formation, stabilization, and relief by phosphorylation of the regulatory light chain.

Artificial protein assemblies with well-defined supramolecular protein nanostructures

Nature uses a wide range of well-defined biomolecular assemblies in diverse cellular processes, where proteins are major building blocks for these supramolecular assemblies. Inspired by their natural counterparts, artificial protein-based assemblies have attracted strong interest as new bio-nanostructures, and strategies to construct ordered protein assemblies have been rapidly expanding. In this review, we provide an overview of very recent studies in the field of artificial protein assemblies, with the particular aim of introducing major assembly methods and unique features of these assemblies. Computational de novo designs were used to build various assemblies with artificial protein building blocks, which are unrelated to natural proteins. Small chemical ligands and metal ions have also been extensively used for strong and bio-orthogonal protein linking. Here, in addition to protein assemblies with well-defined sizes, protein oligomeric and array structures with rather undefined sizes (but with definite repeat protein assembly units) also will be discussed in the context of well-defined protein nanostructures. Lastly, we will introduce multiple examples showing how protein assemblies can be effectively used in various fields such as therapeutics and vaccine development. We believe that structures and functions of artificial protein assemblies will be continuously evolved, particularly according to specific application goals.

Leveraging crosslinking mass spectrometry in structural and cell biology

Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

Stabilization of the SARS-CoV-2 Receptor Binding Domain by Protein Core Redesign and Deep Mutational Scanning.. [PMID: 34845448 PMCID: PMC8629191 DOI: 10.1101/2021.11.22.469552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Stabilizing antigenic proteins as vaccine immunogens or diagnostic reagents is a stringent case of protein engineering and design as the exterior surface must maintain recognition by receptor(s) and antigen—specific antibodies at multiple distinct epitopes. This is a challenge, as stability-enhancing mutations must be focused on the protein core, whereas successful computational stabilization algorithms typically select mutations at solvent-facing positions. In this study we report the stabilization of SARS-CoV-2 Wuhan Hu-1 Spike receptor binding domain (S RBD) using a combination of deep mutational scanning and computational design, including the FuncLib algorithm. Our most successful design encodes I358F, Y365W, T430I, and I513L RBD mutations, maintains recognition by the receptor ACE2 and a panel of different anti-RBD monoclonal antibodies, is between 1–2°C more thermally stable than the original RBD using a thermal shift assay, and is less proteolytically sensitive to chymotrypsin and thermolysin than the original RBD. Our approach could be applied to the computational stabilization of a wide range of proteins without requiring detailed knowledge of active sites or binding epitopes, particularly powerful for cases when there are multiple or unknown binding sites.

Klotho upregulates the interaction between RANK and TRAF6 to facilitate RANKL-induced osteoclastogenesis via the NF-κB signaling pathway

Background

α-Klotho (Klotho) plays a wide range of roles in pathophysiological processes, such as low-turnover osteoporosis observed in klotho mutant mice (kl/kl mice). However, the precise function and underlying mechanism of klotho during osteoclastogenesis are not fully understood. Here, we investigated the effects of klotho on osteoclastogenesis induced by receptor activator of nuclear factor kappa-B ligand (RANKL).

Methods

The effects of klotho deficiency on osteoclastogenesis were explored using kl/kl mice both in vivo and in vitro. In in vitro experiments, lentivirus transfection, real-time quantitative PCR (RT-qPCR) analysis, western blot analysis, immunostaining, RNA-seq analysis, differential pathway analysis, Energy-based protein docking analysis and co-immunoprecipitation were used for deeply investigating the effects of klotho on RANKL-induced Osteoclastogenesis and the underlying mechanism.

Results

We found that klotho deficiency impaired osteoclastogenesis. Furthermore, in vitro studies revealed that klotho facilitated osteoclastogenesis and upregulated the expression of c-Fos and nuclear factor of activated T cells cytoplasmic 1 (NFATc1) during osteoclastogenesis. Mechanistically, we confirmed that klotho co-localized with nuclear factor kappa B (RANK) and facilitated the interaction between activated RANK and TNFR-associated factor 6 (TRAF6), thus klotho exerts its function in osteoclastogenesis through the activation of the NF-κB signaling pathway.

Conclusions

Klotho promotes RANKL-induced osteoclastogenesis through upregulating the interaction between RANK and TARF6, Targeting on klotho may be an attractive therapeutic method for osteopenic diseases.

Paratope states in solution improve structure prediction and docking

Structure-based antibody design and accurate predictions of antibody-antigen interactions remain major challenges in computational biology. By using molecular dynamics simulations, we show that a single static X-ray structure is not sufficient to identify determinants of antibody-antigen recognition. Here, we investigate antibodies that undergo substantial conformational changes upon antigen binding and have been classified as difficult cases in an extensive benchmark for antibody-antigen docking. We present thermodynamics and transition kinetics of these conformational rearrangements and show that paratope states can be used to improve antibody-antigen docking. By using the unbound antibody X-ray structure as starting structure for molecular dynamics simulations, we retain a binding competent conformation substantially different to the unbound antibody X-ray structure. We also observe that the kinetically dominant antibody paratope conformations are chosen by the bound antigen conformation with the highest probability. Thus, we show that paratope states in solution can improve antibody-antigen docking and structure prediction.

Identification of disease-linked hyperactivating mutations in UBE3A through large-scale functional variant analysis

The mechanisms that underlie the extensive phenotypic diversity in genetic disorders are poorly understood. Here, we develop a large-scale assay to characterize the functional valence (gain or loss-of-function) of missense variants identified in UBE3A, the gene whose loss-of-function causes the neurodevelopmental disorder Angelman syndrome. We identify numerous gain-of-function variants including a hyperactivating Q588E mutation that strikingly increases UBE3A activity above wild-type UBE3A levels. Mice carrying the Q588E mutation exhibit aberrant early-life motor and communication deficits, and individuals possessing hyperactivating UBE3A variants exhibit affected phenotypes that are distinguishable from Angelman syndrome. Additional structure-function analysis reveals that Q588 forms a regulatory site in UBE3A that is conserved among HECT domain ubiquitin ligases and perturbed in various neurodevelopmental disorders. Together, our study indicates that excessive UBE3A activity increases the risk for neurodevelopmental pathology and suggests that functional variant analysis can help delineate mechanistic subtypes in monogenic disorders.

UBE3A gene dysregulation is associated with neurodevelopmental disorders, but predicting the function of UBE3A variants remains difficult. The authors use a high-throughput assay to categorize variants by functional activity, and show that UBE3A hyperactivity increases the risk of neurodevelopmental disease.

De novo protein fold families expand the designable ligand binding site space

A major challenge in designing proteins de novo to bind user-defined ligands with high affinity is finding backbones structures into which a new binding site geometry can be engineered with high precision. Recent advances in methods to generate protein fold families de novo have expanded the space of accessible protein structures, but it is not clear to what extend de novo proteins with diverse geometries also expand the space of designable ligand binding functions. We constructed a library of 25,806 high-quality ligand binding sites and developed a fast protocol to place (“match”) these binding sites into both naturally occurring and de novo protein families with two fold topologies: Rossman and NTF2. Each matching step involves engineering new binding site residues into each protein “scaffold”, which is distinct from the problem of comparing already existing binding pockets. 5,896 and 7,475 binding sites could be matched to the Rossmann and NTF2 fold families, respectively. De novo designed Rossman and NTF2 protein families can support 1,791 and 678 binding sites that cannot be matched to naturally existing structures with the same topologies, respectively. While the number of protein residues in ligand binding sites is the major determinant of matching success, ligand size and primary sequence separation of binding site residues also play important roles. The number of matched binding sites are power law functions of the number of members in a fold family. Our results suggest that de novo sampling of geometric variations on diverse fold topologies can significantly expand the space of designable ligand binding sites for a wealth of possible new protein functions.

De novo design of proteins that can bind to novel and highly diverse user-defined small molecule ligands could have broad biomedical and synthetic biology applications. Because ligand binding site geometries need to be accommodated by protein backbone scaffolds at high accuracy, the diversity of scaffolds is a major limitation for designing new ligand binding functions. Advances in computational protein structure design methods have significantly increased the number of accessible stable scaffold structures. Understanding how many new ligand binding sites can be designed into the de novo scaffolds is important for engineering novel ligand binding proteins. To answer this question, we constructed a large library of ligand binding sites from the Protein Data Bank (PDB). We tested the number of ligand binding sites that can be designed into de novo scaffolds and naturally existing scaffolds with the same fold topologies. The results showed that de novo scaffolds significantly expanded the potential ligand binding space of their respective fold topologies. We also identified factors that affect difficulties of binding site accommodation, as well as the relationship between the number of scaffolds and the accessible ligand binding site space. We believe our findings will benefit future method development and applications of ligand binding protein design.

Cross-Linking Mass Spectrometry for Investigating Protein Conformations and Protein-Protein Interactions─A Method for All Seasons

Mass spectrometry (MS) has become one of the key technologies of structural biology. In this review, the contributions of chemical cross-linking combined with mass spectrometry (XL-MS) for studying three-dimensional structures of proteins and for investigating protein-protein interactions are outlined. We summarize the most important cross-linking reagents, software tools, and XL-MS workflows and highlight prominent examples for characterizing proteins, their assemblies, and interaction networks in vitro and in vivo. Computational modeling plays a crucial role in deriving 3D-structural information from XL-MS data. Integrating XL-MS with other techniques of structural biology, such as cryo-electron microscopy, has been successful in addressing biological questions that to date could not be answered. XL-MS is therefore expected to play an increasingly important role in structural biology in the future.

Use of 3D domain swapping in constructing supramolecular metalloproteins

Supramolecules, which are formed by assembling multiple molecules by noncovalent intermolecular interactions instead of covalent bonds, often show additional properties that cannot be exhibited by a single molecule. Supramolecules have evolved into molecular machines in the field of chemistry, and various supramolecular proteins are responsible for life activities in the field of biology. The design and creation of supramolecular proteins will lead to development of new enzymes, functional biomaterials, drug delivery systems, etc.; thus, the number of studies on the regulation of supramolecular proteins is increasing year by year. Several methods, including disulfide bond, metal coordination, and surface-surface interaction, have been utilized to construct supramolecular proteins. In nature, proteins have been shown to form oligomers by 3D domain swapping (3D-DS), a phenomenon in which a structural region is exchanged between molecules of the same protein. We have been studying the mechanism of 3D-DS and utilizing 3D-DS to construct supramolecular metalloproteins. Cytochrome c forms cyclic oligomers and polymers by 3D-DS, whereas other metalloproteins, such as various c-type cytochromes and azurin form small oligomers and myoglobin forms a compact dimer. We have also utilized 3D-DS to construct heterodimers with different active sites, a protein nanocage encapsulating a Zn-SO₄ cluster in the internal cavity, and a tetrahedron with a designed building block protein. Protein oligomer formation was controlled for the 3D-DS dimer of a dimer-monomer transition protein. This article reviews our research on supramolecular metalloproteins.

SARS-CoV-2 spike evolutionary behaviors; simulation of N501Y mutation outcomes in terms of immunogenicity and structural characteristic

Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), a large number of mutations in its genome have been reported. Some of the mutations occur in noncoding regions without affecting the pathobiology of the virus, while mutations in coding regions are significant. One of the regions where a mutation can occur, affecting the function of the virus is at the receptor‐binding domain (RBD) of the spike protein. RBD interacts with angiotensin‐converting enzyme 2 (ACE2) and facilitates the entry of the virus into the host cells. There is a lot of focus on RBD mutations, especially the displacement of N501Y which is observed in the UK/Kent, South Africa, and Brazilian lineages of SARS‐CoV‐2. Our group utilizes computational biology approaches such as immunoinformatics, protein–protein interaction analysis, molecular dynamics, free energy computation, and tertiary structure analysis to disclose the consequences of N501Y mutation at the molecular level. Surprisingly, we discovered that this mutation reduces the immunogenicity of the spike protein; also, displacement of Asn with Tyr reduces protein compactness and significantly increases the stability of the spike protein and its affinity to ACE2. Moreover, following the N501Y mutation secondary structure and folding of the spike protein changed dramatically.

Distinct RPA domains promote recruitment and the helicase-nuclease activities of Dna2

The Dna2 helicase-nuclease functions in concert with the replication protein A (RPA) in DNA double-strand break repair. Using ensemble and single-molecule biochemistry, coupled with structure modeling, we demonstrate that the stimulation of S. cerevisiae Dna2 by RPA is not a simple consequence of Dna2 recruitment to single-stranded DNA. The large RPA subunit Rfa1 alone can promote the Dna2 nuclease activity, and we identified mutations in a helix embedded in the N-terminal domain of Rfa1 that specifically disrupt this capacity. The same RPA mutant is instead fully functional to recruit Dna2 and promote its helicase activity. Furthermore, we found residues located on the outside of the central DNA-binding OB-fold domain Rfa1-A, which are required to promote the Dna2 motor activity. Our experiments thus unexpectedly demonstrate that different domains of Rfa1 regulate Dna2 recruitment, and its nuclease and helicase activities. Consequently, the identified separation-of-function RPA variants are compromised to stimulate Dna2 in the processing of DNA breaks. The results explain phenotypes of replication-proficient but radiation-sensitive RPA mutants and illustrate the unprecedented functional interplay of RPA and Dna2.

An enzymatic ensemble including Dna2 functions in DNA end resection; the function of the single-stranded DNA binding protein RPA in this complex has been underappreciated. Here the authors employ molecular modeling, biochemistry, and single molecule biophysics to reveal RPA directly promotes Dna2 recruitment, nuclease and helicase activities.

Protein structure prediction using residue-resolved protection factors from hydrogen-deuterium exchange NMR

Hydrogen-deuterium exchange (HDX) measured by nuclear magnetic resonance (NMR) provides structural information for proteins relating to solvent accessibility and flexibility. While this structural information is beneficial, the data cannot be used exclusively to elucidate structures. However, the structural information provided by the HDX-NMR data can be supplemented by computational methods. In previous work, we developed an algorithm in Rosetta to predict structures using qualitative HDX-NMR data (categories of exchange rate). Here we expand on the effort, and utilize quantitative protection factors (PFs) from HDX-NMR for structure prediction. From observed correlations between PFs and solvent accessibility/flexibility measures, we present a scoring function to quantify the agreement with HDX data. Using a benchmark set of 10 proteins, an average improvement of 5.13 Å in root-mean-square deviation (RMSD) is observed for cases of inaccurate Rosetta predictions. Ultimately, seven out of 10 predictions are accurate without including HDX data, and nine out of 10 are accurate when using our PF-based HDX score.

Protein Structure Prediction with Mass Spectrometry Data

Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen-deuterium exchange, hydroxyl radical protein footprinting, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Paramagnetic spin labeling of a bacterial DnaB helicase for solid-state NMR

Labeling of biomolecules with a paramagnetic probe for nuclear magnetic resonance (NMR) spectroscopy enables determining long-range distance restraints, which are otherwise not accessible by classically used dipolar coupling-based NMR approaches. Distance restraints derived from paramagnetic relaxation enhancements (PREs) can facilitate the structure determination of large proteins and protein complexes. We herein present the site-directed labeling of the large oligomeric bacterial DnaB helicase from Helicobacter pylori with cysteine-reactive maleimide tags carrying either a nitroxide radical or a lanthanide ion. The success of the labeling reaction was followed by quantitative continuous-wave electron paramagnetic resonance (EPR) experiments performed on the nitroxide-labeled protein. PREs were extracted site-specifically from 2D and 3D solid-state NMR spectra. A good agreement with predicted PRE values, derived by computational modeling of nitroxide and Gd³⁺ tags in the low-resolution DnaB crystal structure, was found. Comparison of experimental PREs and model-predicted spin label-nucleus distances indicated that the size of the "blind sphere" around the paramagnetic center, in which NMR resonances are not detected, is slightly larger for Gd³⁺ (∼14 Å) than for nitroxide (∼11 Å) in ¹³C-detected 2D spectra of DnaB. We also present Gd³⁺-Gd³⁺ dipolar electron-electron resonance EPR experiments on DnaB supporting the conclusion that DnaB was present as a hexameric assembly.

Design of peptides with high affinity binding to a monoclonal antibody as a basis for immunotherapy

About half of the US population is sensitized to one or more allergens, as found by a National Health and Nutrition Examination Survey (NHANES). The most common treatment for seasonal allergic responses is the daily use of oral antihistamines, which can control some of the symptoms, but are not effective for nasal congestion, and can be debilitating in many patients. Peptide immunotherapy is a promising new approach to treat allergic airway diseases. The small size of the immunogens cannot lead to an unwanted allergic reaction in sensitized patients, and the production of peptides with sufficient amounts for immunotherapy is time- and cost-effective. However, it is not known what peptides are the most effective for an immunotherapy of allergens. We previously produced a unique monoclonal antibody (mAb) E58, which can inhibit the binding of multiple groups of mAbs and human IgEs from patients affected by the major group 1 allergens of ragweed (Amb a 1) and conifer pollens (Jun a 1, Cup s 1, and Cry j 1). Here, we demonstrated that a combined approach, starting from two linear E58 epitopes of the tree pollen allergen Jun a 1 and the ragweed pollen allergen Amb a 1, and residue modifications suggested by molecular docking calculations and peptide design could identify a large number of high affinity binding peptides. We propose that this combined experimental and computational approach by structural analysis of linear IgE epitopes and peptide design, can lead to potential new candidates for peptide immunotherapy.

Protein Design with Deep Learning

Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of publicly available protein data. Deep Learning (DL) is a very powerful tool to extract patterns from raw data, provided that data are formatted as mathematical objects and the architecture processing them is well suited to the targeted problem. In the case of protein data, specific representations are needed for both the amino acid sequence and the protein structure in order to capture respectively 1D and 3D information. As no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated DL architecture for design and related tasks.

Streptococcus pyogenes Forms Serotype- and Local Environment-Dependent Interspecies Protein Complexes

Streptococcus pyogenes is known to cause both mucosal and systemic infections in humans. In this study, we used a combination of quantitative and structural mass spectrometry techniques to determine the composition and structure of the interaction network formed between human plasma proteins and the surfaces of different S. pyogenes serotypes. Quantitative network analysis revealed that S. pyogenes forms serotype-specific interaction networks that are highly dependent on the domain arrangement of the surface-attached M protein. Subsequent structural mass spectrometry analysis and computational modeling of one of the M proteins, M28, revealed that the network structure changes across different host microenvironments. We report that M28 binds secretory IgA via two separate binding sites with high affinity in saliva. During vascular leakage mimicked by increasing plasma concentrations in saliva, the binding of secretory IgA was replaced by the binding of monomeric IgA and C4b-binding protein (C4BP). This indicates that an upsurge of C4BP in the local microenvironment due to damage to the mucosal membrane drives the binding of C4BP and monomeric IgA to M28. These results suggest that S. pyogenes has evolved to form microenvironment-dependent host-pathogen protein complexes to combat human immune surveillance during both mucosal and systemic infections.

IMPORTANCEStreptococcus pyogenes (group A Streptococcus [GAS]), is a human-specific Gram-positive bacterium. Each year, the bacterium affects 700 million people globally, leading to 160,000 deaths. The clinical manifestations of S. pyogenes are diverse, ranging from mild and common infections like tonsillitis and impetigo to life-threatening systemic conditions such as sepsis and necrotizing fasciitis. S. pyogenes expresses multiple virulence factors on its surface to localize and initiate infections in humans. Among all these expressed virulence factors, the M protein is the most important antigen. In this study, we perform an in-depth characterization of the human protein interactions formed around one of the foremost human pathogens. This strategy allowed us to decipher the protein interaction networks around different S. pyogenes strains on a global scale and to compare and visualize how such interactions are mediated by M proteins.

Colicin-Mediated Transport of DNA through the Iron Transporter FepA

Colicins are protein antibiotics deployed by Escherichia coli to eliminate competing strains. Colicins frequently exploit outer membrane (OM) nutrient transporters to penetrate the selectively permeable bacterial cell envelope. Here, by applying live-cell fluorescence imaging, we were able to monitor the entry of the pore-forming toxin colicin B (ColB) into E. coli and localize it within the periplasm. We further demonstrate that single-stranded DNA coupled to ColB can also be transported to the periplasm, emphasizing that the import routes of colicins can be exploited to carry large cargo molecules into bacteria. Moreover, we characterize the molecular mechanism of ColB association with its OM receptor FepA by applying a combination of photoactivated cross-linking, mass spectrometry, and structural modeling. We demonstrate that complex formation is coincident with large-scale conformational changes in the colicin. Thereafter, active transport of ColB through FepA involves the colicin taking the place of the N-terminal half of the plug domain that normally occludes this iron transporter. IMPORTANCE Decades of excessive use of readily available antibiotics has generated a global problem of antibiotic resistance and, hence, an urgent need for novel antibiotic solutions. Bacteriocins are protein-based antibiotics produced by bacteria to eliminate closely related competing bacterial strains. Bacteriocin toxins have evolved to bypass the complex cell envelope in order to kill bacterial cells. Here, we uncover the cellular penetration mechanism of a well-known but poorly understood bacteriocin called colicin B that is active against Escherichia coli. Moreover, we demonstrate that the colicin B-import pathway can be exploited to deliver conjugated DNA cargo into bacterial cells. Our work leads to a better understanding of the way bacteriocins, as potential alternative antibiotics, execute their mode of action as well as highlighting how they might even be exploited in the genomic manipulation of Gram-negative bacteria.

Molecular coevolution of nuclear and nucleolar localization signals inside basic domain of HIV-1 Tat

During evolution, viruses had to adapt to an increasingly complex environment of eukaryotic cells. Viral proteins that need to enter the cell nucleus or associate with nucleoli possess nuclear localization signals (NLSs) and nucleolar localization signals (NoLSs) for nuclear and nucleolar accumulation, respectively. As viral proteins are relatively small, acquisition of novel sequences seems to be a more complicated task for viruses than for eukaryotes. Here, we carried out a comprehensive analysis of the basic domain (BD) of HIV-1 Tat to show how viral proteins might evolve with NLSs and NoLSs without an increase in protein size. The HIV-1 Tat BD is involved in several functions, the most important being the transactivation of viral transcription. The BD also functions as an NLS, although it is substantially longer than a typical NLS. It seems that different regions in the BD could function as NLSs due to its enrichment with positively charged amino acids. Additionally, the high positive net charge inevitably causes the BD to function as an NoLS through a charge-specific mechanism. The integration of NLSs and NoLSs into functional domains enriched with positively charged amino acids might be a mechanism that allows the condensation of different functional sequences in small protein regions and, as a result, to reduce protein size, influencing the origin and evolution of NLSs and NoLSs in viruses. IMPORTANCE Here, we investigated the molecular mechanism of NLS and NoLS integration into the basic domain of HIV-1 Tat (⁴⁹RKKRRQRRR⁵⁷), and found that these two supplementary functions (i.e., function of NLS and NoLS) are embedded in the basic domain amino acid sequence. The integration of NLSs and NoLSs into functional domains of viral proteins enriched with positively charged amino acids is a mechanism that allows the concentration of different functions within small protein regions. Integration of NLS and NoLS into functional protein domains might have influenced the viral evolution, as this could prevent an increase in the protein size.

Computational Design of Structured and Functional Peptide Macrocycles

Structure-based computational design methods have been developed to create proteins in silico with diverse shapes and sizes that accurately fold in vitro, from 7-residue macrocycles to megadalton-scale self-assembling nanomaterials. Precise control over protein shape has further enabled design and optimization of functional therapeutic proteins, including agonists, antagonists, enzymes, and vaccines. Computational design of functional peptides of smaller size presents a persistent challenge, with few successful examples to date. Herein we describe validated general methods for computational design of peptides using the Rosetta molecular modeling suite and discuss outstanding challenges and future directions.

Synthetic Biology Approaches for Engineering Next-Generation Adenoviral Gene Therapies

Synthetic biology centers on the design and modular assembly of biological parts so as to construct artificial biological systems. Over the past decade, synthetic biology has blossomed into a highly productive field, yielding advances in diverse areas such as neuroscience, cell-based therapies, and chemical manufacturing. Similarly, the field of gene therapy has made enormous strides both in proof-of-concept studies and in the clinical setting. One viral vector of increasing interest for gene therapy is the adenovirus (Ad). A major part of the Ad's increasing momentum comes from synthetic biology approaches to Ad engineering. Convergence of gene therapy and synthetic biology has enhanced Ad vectors by mitigating Ad toxicity in vivo, providing precise Ad tropisms, and incorporating genetic circuits to make smart therapies which adapt to environmental stimuli. Synthetic biology engineering of Ad vectors may lead to superior gene delivery and editing platforms which could find applications in a wide range of therapeutic contexts.

Computationally Designed ACE2 Decoy Receptor Binds SARS-CoV-2 Spike (S) Protein with Tight Nanomolar Affinity

Even with the availability of vaccines, therapeutic options for COVID-19 still remain highly desirable, especially in hospitalized patients with moderate or severe disease. Soluble ACE2 (sACE2) is a promising therapeutic candidate that neutralizes SARS CoV-2 infection by acting as a decoy. Using computational mutagenesis, we designed a number of sACE2 derivatives carrying three to four mutations. The top-predicted sACE2 decoy based on the in silico mutagenesis scan was subjected to molecular dynamics and free-energy calculations for further validation. After illuminating the mechanism of increased binding for our designed sACE2 derivative, the design was verified experimentally by flow cytometry and BLI-binding experiments. The computationally designed sACE2 decoy (ACE2-FFWF) bound the receptor-binding domain of SARS-CoV-2 tightly with low nanomolar affinity and ninefold affinity enhancement over the wild type. Furthermore, cell surface expression was slightly greater than wild-type ACE2, suggesting that the design is well-folded and stable. Having an arsenal of high-affinity sACE2 derivatives will help to buffer against the emergence of SARS CoV-2 variants. Here, we show that computational methods have become sufficiently accurate for the design of therapeutics for current and future viral pandemics.

Integration of Mass Spectrometry Data for Structural Biology

Mass spectrometry (MS) is increasingly being used to probe the structure and dynamics of proteins and the complexes they form with other macromolecules. There are now several specialized MS methods, each with unique sample preparation, data acquisition, and data processing protocols. Collectively, these methods are referred to as structural MS and include cross-linking, hydrogen-deuterium exchange, hydroxyl radical footprinting, native, ion mobility, and top-down MS. Each of these provides a unique type of structural information, ranging from composition and stoichiometry through to residue level proximity and solvent accessibility. Structural MS has proved particularly beneficial in studying protein classes for which analysis by classic structural biology techniques proves challenging such as glycosylated or intrinsically disordered proteins. To capture the structural details for a particular system, especially larger multiprotein complexes, more than one structural MS method with other structural and biophysical techniques is often required. Key to integrating these diverse data are computational strategies and software solutions to facilitate this process. We provide a background to the structural MS methods and briefly summarize other structural methods and how these are combined with MS. We then describe current state of the art approaches for the integration of structural MS data for structural biology. We quantify how often these methods are used together and provide examples where such combinations have been fruitful. To illustrate the power of integrative approaches, we discuss progress in solving the structures of the proteasome and the nuclear pore complex. We also discuss how information from structural MS, particularly pertaining to protein dynamics, is not currently utilized in integrative workflows and how such information can provide a more accurate picture of the systems studied. We conclude by discussing new developments in the MS and computational fields that will further enable in-cell structural studies.

Computational Redesign of an ω-Transaminase from Pseudomonas jessenii for Asymmetric Synthesis of Enantiopure Bulky Amines

ω-Transaminases (ω-TA) are attractive biocatalysts for the production of chiral amines from prochiral ketones via asymmetric synthesis. However, the substrate scope of ω-TAs is usually limited due to steric hindrance at the active site pockets. We explored a protein engineering strategy using computational design to expand the substrate scope of an (S)-selective ω-TA from Pseudomonas jessenii (PjTA-R6) toward the production of bulky amines. PjTA-R6 is attractive for use in applied biocatalysis due to its thermostability, tolerance to organic solvents, and acceptance of high concentrations of isopropylamine as amino donor. PjTA-R6 showed no detectable activity for the synthesis of six bicyclic or bulky amines targeted in this study. Six small libraries composed of 7–18 variants each were separately designed via computational methods and tested in the laboratory for ketone to amine conversion. In each library, the vast majority of the variants displayed the desired activity, and of the 40 different designs, 38 produced the target amine in good yield with >99% enantiomeric excess. This shows that the substrate scope and enantioselectivity of PjTA mutants could be predicted in silico with high accuracy. The single mutant W58G showed the best performance in the synthesis of five structurally similar bulky amines containing the indan and tetralin moieties. The best variant for the other bulky amine, 1-phenylbutylamine, was the triple mutant W58M + F86L + R417L, indicating that Trp58 is a key residue in the large binding pocket for PjTA-R6 redesign. Crystal structures of the two best variants confirmed the computationally predicted structures. The results show that computational design can be an efficient approach to rapidly expand the substrate scope of ω-TAs to produce enantiopure bulky amines.

A New World Monkey Resembles Human in Bitter Taste Receptor Evolution and Function via a Single Parallel Amino Acid Substitution

Bitter taste receptors serve as a vital component in the defense system against toxin intake by animals, and the family of genes encoding these receptors has been demonstrated, usually by family size variance, to correlate with dietary preference. However, few systematic studies of specific Tas2R to unveil their functional evolution have been conducted. Here, we surveyed Tas2R16 across all major clades of primates and reported a rare case of a convergent change to increase sensitivity to β-glucopyranosides in human and a New World monkey, the white-faced saki. Combining analyses at multiple levels, we demonstrate that a parallel amino acid substitution (K172N) shared by these two species is responsible for this functional convergence of Tas2R16. Considering the specialized feeding preference of the white-faced saki, the K172N change likely played an important adaptive role in its early evolution to avoid potentially toxic cyanogenic glycosides, as suggested for the human TAS2R16 gene.

XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers

Graph representations are traditionally used to represent protein structures in sequence design protocols in which the protein backbone conformation is known. This infrequently extends to machine learning projects: existing graph convolution algorithms have shortcomings when representing protein environments. One reason for this is the lack of emphasis on edge attributes during massage-passing operations. Another reason is the traditionally shallow nature of graph neural network architectures. Here we introduce an improved message-passing operation that is better equipped to model local kinematics problems such as protein design. Our approach, XENet, pays special attention to both incoming and outgoing edge attributes. We compare XENet against existing graph convolutions in an attempt to decrease rotamer sample counts in Rosetta's rotamer substitution protocol, used for protein side-chain optimization and sequence design. This use case is motivating because it both reduces the size of the search space for classical side-chain optimization algorithms, and allows larger protein design problems to be solved with quantum algorithms on near-term quantum computers with limited qubit counts. XENet outperformed competing models while also displaying a greater tolerance for deeper architectures. We found that XENet was able to decrease rotamer counts by 40% without loss in quality. This decreased the memory consumption for classical pre-computation of rotamer energies in our use case by more than a factor of 3, the qubit consumption for an existing sequence design quantum algorithm by 40%, and the size of the solution space by a factor of 165. Additionally, XENet displayed an ability to handle deeper architectures than competing convolutions.

Structural and energetic profiling of SARS-CoV-2 receptor binding domain antibody recognition and the impact of circulating variants

The SARS-CoV-2 pandemic highlights the need for a detailed molecular understanding of protective antibody responses. This is underscored by the emergence and spread of SARS-CoV-2 variants, including Alpha (B.1.1.7) and Delta (B.1.617.2), some of which appear to be less effectively targeted by current monoclonal antibodies and vaccines. Here we report a high resolution and comprehensive map of antibody recognition of the SARS-CoV-2 spike receptor binding domain (RBD), which is the target of most neutralizing antibodies, using computational structural analysis. With a dataset of nonredundant experimentally determined antibody-RBD structures, we classified antibodies by RBD residue binding determinants using unsupervised clustering. We also identified the energetic and conservation features of epitope residues and assessed the capacity of viral variant mutations to disrupt antibody recognition, revealing sets of antibodies predicted to effectively target recently described viral variants. This detailed structure-based reference of antibody RBD recognition signatures can inform therapeutic and vaccine design strategies.

How much can physics do for protein design?

Physics and physical chemistry are an important thread in computational protein design, complementary to knowledge-based tools. They provide molecular mechanics scoring functions that need little or no ad hoc parameter readjustment, methods to thoroughly sample equilibrium ensembles, and different levels of approximation for conformational flexibility. They led recently to the successful redesign of a small protein using a physics-based folded state energy. Adaptive Monte Carlo or molecular dynamics schemes were discovered where protein variants are populated as per their ligand-binding free energy or catalytic efficiency. Molecular dynamics have been used for backbone flexibility. Implicit solvent models have been refined, polarizable force fields applied, and many physical insights obtained.

Rationalizing the Unprecedented Stereochemistry of an Enzymatic Nitrile Synthesis through a Combined Computational and Experimental Approach

In this contribution, the unique and unprecedented stereochemical phenomenon of an aldoxime dehydratase‐catalyzed enantioselective dehydration of racemic E‐ and Z‐aldoximes with selective formation of both enantiomeric forms of a chiral nitrile is rationalized by means of molecular modelling, comprising in silico mutations and docking studies. This theoretical investigation gave detailed insight into why with the same enzyme the use of racemic E‐ and Z‐aldoximes leads to opposite forms of the chiral nitrile. The calculated mutants with a larger or smaller cavity in the active site were then prepared and used in biotransformations, showing the theoretically predicted decrease and increase of the enantioselectivities in these nitrile syntheses. This validated model also enabled the rational design of mutants with a smaller cavity, which gave superior enantioselectivities compared to the known wild‐type enzyme, with excellent E‐values of up to E>200 when the mutant OxdRE‐Leu145Phe was utilized.