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Many membrane proteins are prone to misfolding, which compromises their functional expression at the plasma membrane. This is particularly true for the mammalian gonadotropin-releasing hormone receptor GPCRs (GnRHR). We recently demonstrated that evolutionary GnRHR modifications appear to have coincided with adaptive changes in cotranslational folding efficiency. Though protein stability is known to shape evolution, it is unclear how cotranslational folding constraints modulate the synergistic, epistatic interactions between mutations. We therefore compared the pairwise interactions formed by mutations that disrupt the membrane topology (V276T) or tertiary structure (W107A) of GnRHR. Using deep mutational scanning, we evaluated how the plasma membrane expression of these variants is modified by hundreds of secondary mutations. An analysis of 251 mutants in three genetic backgrounds reveals that V276T and W107A form distinct epistatic interactions that depend on both the severity and the mechanism of destabilization. V276T forms predominantly negative epistatic interactions with destabilizing mutations in soluble loops. In contrast, W107A forms positive interactions with mutations in both loops and transmembrane domains that reflect the diminishing impacts of the destabilizing mutations in variants that are already unstable. These findings reveal how epistasis is remodeled by conformational defects in membrane proteins and in unstable proteins more generally.

Abstract In the rapidly advancing field of synthetic biology, there exists a critical need for technology to discover targeting moieties for therapeutic biologics. Here we present INSPIRE-seq, an approach that utilizes a nanobody library and next-generation sequencing to identify nanobodies selected for complex environments. INSPIRE-seq enables the parallel enrichment of immune cell-binding nanobodies that penetrate the tumor microenvironment. Clone enrichment and specificity vary across immune cell subtypes in the tumor, lymph node, and spleen. INSPIRE-seq identifies a dendritic cell binding clone that binds PHB2. Single-cell RNA sequencing reveals a connection with cDC1s, and immunofluorescence confirms nanobody-PHB2 colocalization along cell membranes. Structural modeling and docking studies assist binding predictions and will guide nanobody selection. In this work, we demonstrate that INSPIRE-seq offers an unbiased approach to examine complex microenvironments and assist in the development of nanobodies, which could serve as active drugs, modified to become drugs, or used as targeting moieties.

Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta's protein engineering toolbox that allow for the rational design of PTMs.

Delivery to the correct membrane domain in polarized epithelial cells is a critical regulatory mechanism for transmembrane proteins. The trafficking of these proteins is directed by short amino acid sequences known as sorting motifs. In six basolaterally-localized proteins lacking the canonical tyrosine- and dileucine-based basolateral sorting motifs, a monoleucine-based sorting motif has been identified. This review will discuss these proteins with an identified monoleucine-based sorting motif, their conserved structural features, as well as the future directions of study for this non-canonical basolateral sorting motif.

Variants in the cystic fibrosis transmembrane conductance regulator gene (CFTR) result in cystic fibrosis-a lethal autosomal recessive disorder. Missense variants that alter a single amino acid in the CFTR protein are among the most common cystic fibrosis variants, yet tools for accurately predicting molecular consequences of missense variants have been limited to date. AlphaMissense (AM) is a new technology that predicts the pathogenicity of missense variants based on dual learned protein structure and evolutionary features. Here, we evaluated the ability of AM to predict the pathogenicity of CFTR missense variants. AM predicted a high pathogenicity for CFTR residues overall, resulting in a high false positive rate and fair classification performance on CF variants from the CFTR2.org database. AM pathogenicity score correlated modestly with pathogenicity metrics from persons with CF including sweat chloride level, pancreatic insufficiency rate, and Pseudomonas aeruginosa infection rate. Correlation was also modest with CFTR trafficking and folding competency in vitro. By contrast, the AM score correlated well with CFTR channel function in vitro-demonstrating the dual structure and evolutionary training approach learns important functional information despite lacking such data during training. Different performance across metrics indicated AM may determine if polymorphisms in CFTR are recessive CF variants yet cannot differentiate mechanistic effects or the nature of pathophysiology. Finally, AM predictions offered limited utility to inform on the pharmacological response of CF variants i.e., theratype. Development of new approaches to differentiate the biochemical and pharmacological properties of CFTR variants is therefore still needed to refine the targeting of emerging precision CF therapeutics.

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Identifying determinants of broadly neutralizing antibodies against hepatitis C virus (HCV) may guide HCV vaccine design. Here, the authors discover new anti-HCV antibodies using computational screening and analyze the amino acid composition and sequence-structure relationships in this antibody family.

Hantaviruses are high-priority emerging pathogens carried by rodents and transmitted to humans by aerosolized excreta or, in rare cases, person-to-person contact. While infections in humans are relatively rare, mortality rates range from 1 to 40% depending on the hantavirus species. There are currently no FDA-approved vaccines or therapeutics for hantaviruses, and the only treatment for infection is supportive care for respiratory or kidney failure. Additionally, the human humoral immune response to hantavirus infection is incompletely understood, especially the location of major antigenic sites on the viral glycoproteins and conserved neutralizing epitopes. Here, we report antigenic mapping and functional characterization for four neutralizing hantavirus antibodies. The broadly neutralizing antibody SNV-53 targets an interface between Gn/Gc, neutralizes through fusion inhibition and cross-protects against the Old World hantavirus species Hantaan virus when administered pre- or post-exposure. Another broad antibody, SNV-24, also neutralizes through fusion inhibition but targets domain I of Gc and demonstrates weak neutralizing activity to authentic hantaviruses. ANDV-specific, neutralizing antibodies (ANDV-5 and ANDV-34) neutralize through attachment blocking and protect against hantavirus cardiopulmonary syndrome (HCPS) in animals but target two different antigenic faces on the head domain of Gn. Determining the antigenic sites for neutralizing antibodies will contribute to further therapeutic development for hantavirus-related diseases and inform the design of new broadly protective hantavirus vaccines.

Lipid molecules such as cholesterol interact with the surface of integral membrane proteins (IMP) in a mode different from drug-like molecules in a protein binding pocket. These differences are due to the lipid molecule's shape, the membrane's hydrophobic environment, and the lipid's orientation in the membrane. We can use the recent increase in experimental structures in complex with cholesterol to understand protein-cholesterol interactions. We developed the RosettaCholesterol protocol consisting of (1) a prediction phase using an energy grid to sample and score native-like binding poses and (2) a specificity filter to calculate the likelihood that a cholesterol interaction site may be specific. We used a multi-pronged benchmark (self-dock, flip-dock, cross-dock, and global-dock) of protein-cholesterol complexes to validate our method. RosettaCholesterol improved sampling and scoring of native poses over the standard RosettaLigand baseline method in 91% of cases and performs better regardless of benchmark complexity. On the β2AR, our method found one likely-specific site, which is described in the literature. The RosettaCholesterol protocol quantifies cholesterol binding site specificity. Our approach provides a starting point for high-throughput modeling and prediction of cholesterol binding sites for further experimental validation.

Glycan masking is a novel technique in reverse vaccinology in which sugar chains (glycans) are added on the surface of immunogen candidates to hide regions of low interest and thus focus the immune system on highly therapeutic epitopes. This shielding strategy is inspired by viruses such as influenza and HIV, which are able to escape the immune system by incorporating additional glycosylation and preventing the binding of therapeutic antibodies. Interestingly, the glycan masking technique is mainly used in vaccine design to fight the same viruses that naturally use glycans to evade the immune system. In this review we report the major successes obtained with the glycan masking technique in epitope-focused vaccine design. We focus on the choice of the target antigen, the strategy for immunogen design and the relevance of the carrier vector to induce a strong immune response. Moreover, we will elucidate the different applications that can be accomplished with glycan masking, such as shifting the immune response from hyper-variable epitopes to more conserved ones, focusing the response on known therapeutic epitopes, broadening the response to different viral strains/sub-types and altering the antigen immunogenicity to elicit higher or lower immune response, as desired.

Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge in structural biology. While integrative structural biology has been the most effective way to get a high-accuracy structure of different conformations and mechanistic insights for larger proteins, advances in deep machine-learning algorithms have paved the way to fully computational predictions. In this field, AlphaFold2 (AF2) pioneered ab initio high-accuracy single-chain modeling. Since then, different customizations have expanded the number of conformational states accessible through AF2. Here, we further expanded AF2 with the aim of enriching an ensemble of models with user-defined functional or structural features. We tackled two common protein families for drug discovery, G-protein-coupled receptors (GPCRs) and kinases. Our approach automatically identifies the best templates satisfying the specified features and combines those with genetic information. We also introduced the possibility of shuffling the selected templates to expand the space of solutions. In our benchmark, models showed the intended bias and great accuracy. Our protocol can thus be exploited for modeling user-defined conformational states in an automatic fashion.

Computational methods are becoming an increasingly important part of biological research. Using the Rosetta framework as an example, the authors demonstrate how community-driven development of computational methods can be done in a reproducible and reliable fashion.

Antibody epitope mapping of viral proteins plays a vital role in understanding immune system mechanisms of protection. In the case of class I viral fusion proteins, recent advances in cryo-electron microscopy and protein stabilization techniques have highlighted the importance of cryptic or 'alternative' conformations that expose epitopes targeted by potent neutralizing antibodies. Thorough epitope mapping of such metastable conformations is difficult but is critical for understanding sites of vulnerability in class I fusion proteins that occur as transient conformational states during viral attachment and fusion. We introduce a novel method Accelerated class I fusion protein Epitope Mapping (AxIEM) that accounts for fusion protein flexibility to improve out-of-sample prediction of discontinuous antibody epitopes. Harnessing data from previous experimental epitope mapping efforts of several class I fusion proteins, we demonstrate that accuracy of epitope prediction depends on residue environment and allows for the prediction of conformation-dependent antibody target residues. We also show that AxIEM can identify common epitopes and provide structural insights for the development and rational design of vaccines.

The human adaptive immune response enables the targeting of epitopes on pathogens with high specificity. Infection with a pathogen induces somatic hyper-mutation and B-cell selection processes that govern the shape and diversity of the antibody sequence landscape. To date, even the largest immunome repertoires of adaptive immune receptors acquired by next-generation sequencing cannot fully capture the vast antibody sequence space of a single individual, which is estimated to be at least 1012 potential sequences. Degeneracy of the genetic code means that the number of possible nucleotide triplets (64) is greater than the number of canonical amino acids (20), resulting in some amino acids being encoded by multiple triplets and different amino acids sharing the same nucleotide in 1 or 2 positions in the triplet. We hypothesize that the degeneracy of the genetic code can be used to statistically model an enlarged space of human antibody amino acid sequences, accommodating for the discrepancy between the observed and the hypothesized antibody sequence space. Facilitated by Bayesian statistics and immunome repertoire clustering, we calculated amino acid probabilities from single nucleotide frequencies to infer a human amino acid sequence space that is used to design human-like antibodies with Rosetta. We show that antibodies designed with our restraints are on average up to 16.6% more human-like in the V and J regions compared to the Rosetta designs produced without constraints. The human-likeness of the heavy-chain CDR3 region (CDRH3) could be increased for 8 of 27 antibodies compared to Rosetta designs with a similar number of mutations and could be successfully applied on Mus musculus antibodies to demonstrate humanization.

Summary: Whole-exome sequencing (WES) in the clinic has identified several rare monogenic developmental and epileptic encephalopathies (DEE) caused by ion channel variants. However, WES often fails to provide actionable insight for rare diseases, such as DEEs, due to the challenges of interpreting variants of unknown significance (VUS). Here, we describe a “personalized structural biology” (PSB) approach that leverages recent innovations in the analysis of protein 3D structures to address this challenge. We illustrate this approach in an Undiagnosed Diseases Network (UDN) individual with DEE symptoms and a de novo VUS in KCNC2 (p.V469L), the Kv3.2 voltage-gated potassium channel. A nearby KCNC2 variant (p.V471L) was recently suggested to cause DEE-like phenotypes. Computational structural modeling suggests that both affect protein function. However, despite their proximity, the p.V469L variant is likely to sterically block the channel pore, while the p.V471L variant is likely to stabilize the open state. Biochemical and electrophysiological analyses demonstrate heterogeneous loss-of-function and gain-of-function effects, as well as differential response to 4-aminopyridine treatment. Molecular dynamics simulations illustrate that the pore of the p.V469L variant is more constricted, increasing the energetic barrier for K+ permeation, whereas the p.V471L variant stabilizes the open conformation. Our results implicate variants in KCNC2 as causative for DEE and guide the interpretation of a UDN individual. They further delineate the molecular basis for the heterogeneous clinical phenotypes resulting from two proximal pathogenic variants. This demonstrates how the PSB approach can provide an analytical framework for individualized hypothesis-driven interpretation of protein-coding VUS.

Summary: Bromocriptine is approved as a diabetes therapy, yet its therapeutic mechanisms remain unclear. Though bromocriptine’s actions have been mainly attributed to the stimulation of brain dopamine D2 receptors (D2R), bromocriptine also targets the pancreas. Here, we employ bromocriptine as a tool to elucidate the roles of catecholamine signaling in regulating pancreatic hormone secretion. In β-cells, bromocriptine acts on D2R and α2A-adrenergic receptor (α2A-AR) to reduce glucose-stimulated insulin secretion (GSIS). Moreover, in α-cells, bromocriptine acts via D2R to reduce glucagon secretion. α2A-AR activation by bromocriptine recruits an ensemble of G proteins with no β-arrestin2 recruitment. In contrast, D2R recruits G proteins and β-arrestin2 upon bromocriptine stimulation, demonstrating receptor-specific signaling. Docking studies reveal distinct bromocriptine binding to α2A-AR versus D2R, providing a structural basis for bromocriptine’s dual actions on β-cell α2A-AR and D2R. Together, joint dopaminergic and adrenergic receptor actions on α-cell and β-cell hormone release provide a new therapeutic mechanism to improve dysglycemia.

G protein-coupled receptors (GPCRs) are the largest class of drug targets and undergo substantial conformational changes in response to ligand binding. Despite recent progress in GPCR structure determination, static snapshots fail to reflect the conformational space of putative binding pocket geometries to which small molecule ligands can bind. In comparative modeling of GPCRs in the absence of a ligand, often a shrinking of the orthosteric binding pocket is observed. However, the exact prediction of the flexible orthosteric binding site is crucial for adequate structure-based drug discovery. In order to improve ligand docking and guide virtual screening experiments in computer-aided drug discovery, we developed RosettaGPCRPocketSize. The algorithm creates a conformational ensemble of biophysically realistic conformations of the GPCR binding pocket between the TM bundle, which is consistent with a knowledge base of expected pocket geometries. Specifically, tetrahedral volume restraints are defined based on information about critical residues in the orthosteric binding site and their experimentally observed range of Cα-Cα-distances. The output of RosettaGPCRPocketSize is an ensemble of binding pocket geometries that are filtered by energy to ensure biophysically probable arrangements, which can be used for docking simulations. In a benchmark set, pocket shrinkage observed in the default RosettaGPCR was reduced by up to 80% and the binding pocket volume range and geometric diversity were increased. Compared to models from four different GPCR homology model databases (RosettaGPCR, GPCR-Tasser, GPCR-SSFE, and GPCRdb), the here-created models showed more accurate volumes of the orthosteric pocket when evaluated with respect to the crystallographic reference structure. Furthermore, RosettaGPCRPocketSize was able to generate an improved realistic pocket distribution. However, while being superior to other homology models, the accuracy of generated model pockets was comparable to AlphaFold2 models. Furthermore, in a docking benchmark using small-molecule ligands with a higher molecular weight between 400 and 700 Da, a higher success rate in creating native-like binding poses was observed. In summary, RosettaGPCRPocketSize can generate GPCR models with realistic orthosteric pocket volumes, which are useful for structure-based drug discovery applications.

Although computational structure prediction has had great successes in recent years, it regularly fails to predict the interactions of large protein complexes with residue-level accuracy, or even the correct orientation of the protein partners. The performance of computational docking can be notably enhanced by incorporating experimental data from structural biology techniques. A rapid method to probe protein-protein interactions is hydrogen-deuterium exchange mass spectrometry (HDX-MS). HDX-MS has been increasingly used for epitope-mapping of antibodies (Abs) to their respective antigens (Ags) in the past few years. In this paper, we review the current state of HDX-MS in studying protein interactions, specifically Ab-Ag interactions, and how it has been used to inform computational structure prediction calculations. Particularly, we address the limitations of HDX-MS in epitope mapping and techniques and protocols applied to overcome these barriers. Furthermore, we explore computational methods that leverage HDX-MS to aid structure prediction, including the computational simulation of HDX-MS data and the combination of HDX-MS and protein docking. We point out challenges in interpreting and incorporating HDX-MS data into Ab-Ag complex docking and highlight the opportunities they provide to build towards a more optimized hybrid method, allowing for more reliable, high throughput epitope identification.

The three human pathogenic ebolaviruses: Zaire (EBOV), Bundibugyo (BDBV), and Sudan (SUDV) virus, cause severe disease with high fatality rates. Epitopes of ebolavirus glycoprotein (GP) recognized by antibodies with binding breadth for all three ebolaviruses are of major interest for rational vaccine design. In particular, the heptad repeat 2 -membrane-proximal external region (HR2-MPER) epitope is relatively conserved between EBOV, BDBV, and SUDV GP and targeted by human broadly-neutralizing antibodies. To study whether this epitope can serve as an immunogen for the elicitation of broadly-reactive antibody responses, protein design in Rosetta was employed to transplant the HR2-MPER epitope identified from a co-crystal structure with the known broadly-reactive monoclonal antibody (mAb) BDBV223 onto smaller scaffold proteins. From computational analysis, selected immunogen designs were produced as recombinant proteins and functionally validated, leading to the identification of a sterile alpha motif (SAM) domain displaying the BDBV-HR2-MPER epitope near its C terminus as a promising candidate. The immunogen was fused to one component of a self-assembling, two-component nanoparticle and tested for immunogenicity in rabbits. Robust titers of cross-reactive serum antibodies to BDBV and EBOV GPs and moderate titers to SUDV GP were induced following immunization. To confirm the structural composition of the immunogens, solution NMR studies were conducted and revealed structural flexibility in the C-terminal residues of the epitope. Overall, our study represents the first report on an epitope-focused immunogen design based on the structurally challenging BDBV-HR2-MPER epitope.

Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design – instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.

Recent advances in experimental and computational protein structure determination have provided access to high-quality structures for most human proteins and mutants thereof. However, linking changes in structure in protein mutants to functional impact remains an active area of method development. If successful, such methods can ultimately assist physicians in taking appropriate treatment decisions. This work presents three artificial neural network (ANN)-based predictive models that classify four key functional parameters of KCNQ1 variants as normal or dysfunctional using PSSM-based evolutionary and/or biophysical descriptors. Recent advances in predicting protein structure and variant properties with artificial intelligence (AI) rely heavily on the availability of evolutionary features and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1 potassium channels that performs comparably or better than algorithms using only on PSSM-based evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of PSSM-based evolutionary features in predicting activation voltage dependence and deactivation time. As AI is increasingly applied to problems in biology, biophysical understanding will be critical with respect to 'explainable AI', i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org.

An EGFR mutant with kinase domain duplication (EGFR-KDD) was previously identified in an index patient, but the functional and therapeutic implications remain unclear. Here, the authors show that KDD occurs in other ErbB receptors in multiple cancers, and characterize the mechanism and inhibition of EGFR-KDD.

Integrins and discoidin domain receptors (DDRs) 1 and 2 promote cell adhesion and migration on both fibrillar and non fibrillar collagens. Collagen I contains DDR and integrin selective binding motifs; however, the relative contribution of these two receptors in regulating cell migration is unclear. DDR1 has five isoforms (DDR1a-e), with most cells expressing the DDR1a and DDR1b isoforms. We show that human embryonic kidney 293 cells expressing DDR1b migrate more than DDR1a expressing cells on DDR selective substrata as well as on collagen I in vitro. In addition, DDR1b expressing cells show increased lung colonization after tail vein injection in nude mice. DDR1a and DDR1b differ from each other by an extra 37 amino acids in the DDR1b cytoplasmic domain. Interestingly, these 37 amino acids contain an NPxY motif which is a central control module within the cytoplasmic domain of β integrins and acts by binding scaffold proteins, including talin. Using purified recombinant DDR1 cytoplasmic tail proteins, we show that DDR1b directly binds talin with higher affinity than DDR1a. In cells, DDR1b, but not DDR1a, colocalizes with talin and integrin β1 to focal adhesions and enhances integrin β1-mediated cell migration. Moreover, we show that DDR1b promotes cell migration by enhancing Rac1 activation. Mechanistically DDR1b interacts with the GTPase-activating protein (GAP) Breakpoint cluster region protein (BCR) thus reducing its GAP activity and enhancing Rac activation. Our study identifies DDR1b as a major driver of cell migration and talin and BCR as key players in the interplay between integrins and DDR1b in regulating cell migration.

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward- and outward-facing states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein-coupled receptors that are absent from the AF2 training set. Whereas models of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.

The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic open-source software package designed to integrate traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a detailed introduction to core BCL cheminformatics functionality, showing how traditional tasks (e.g., computing chemical properties, estimating druglikeness) can be readily combined with machine learning. In addition, we have included multiple examples covering areas of advanced use, such as reaction-based library design. We anticipate that this manuscript will be a valuable resource for researchers in computer-aided drug discovery looking to integrate modular cheminformatics and machine learning tools into their pipelines.

Computation methods that predict the binding of peptides to MHC-I are important tools for screening and identifying immunogenic antigens and have the potential to accelerate vaccine and drug development. However, most available tools are sequence-based and optimized only for peptides containing the twenty canonical amino acids. This omits a large number of peptides containing non-canonical amino acids (NCAA), or residues that undergo varied post-translational modifications such as glycosylation or phosphorylation. These modifications fundamentally alter peptide immunogenicity. Similarly, existing structure-based methods are biased towards canonical peptide backbone structures, which may or may not be preserved when NCAAs are present. Rosetta FlexPepDock ab-initio is a structure-based computational protocol able to evaluate peptide-receptor interaction where no prior information of the peptide backbone is known. We benchmarked FlexPepDock ab-initio for docking canonical peptides to MHC-I, and illustrate for the first time the method's ability to accurately model MHC-I bound epitopes containing NCAAs. FlexPepDock ab-initio protocol was able to recapitulate near-native structures (≤1.5Å) in the top lowest-energy models for 20 out of 25 cases in our initial benchmark. Using known experimental binding affinities of twenty peptides derived from an influenza-derived peptide, we showed that FlexPepDock protocol is able to predict relative binding affinity as Rosetta energies correlate well with experimental values (r = 0.59, p = 0.006). ROC analysis revealed 80% true positive and a 40% false positive rate, with a prediction power of 93%. Finally, we demonstrate the protocol's ability to accurately recapitulate HLA-A*02:01 bound phosphopeptide backbone structures and relative binding affinity changes, the theoretical structure of the lymphocytic choriomeningitis derived glycosylated peptide GP392 bound to MHC-I H-2Db, and isolevuglandin-adducted peptides. The ability to use non-canonical amino acids in the Rosetta FlexPepDock protocol may provide useful insight into critical amino acid positions where the post-translational modification modulates immunologic responses.

Water molecules at the protein-small molecule interface often form hydrogen bonds with both the small molecule ligand and the protein, affecting the structural integrity and energetics of a binding event. The inclusion of these ‘bridging waters’ has been shown to improve the accuracy of predicted docked structures; however, due to increased computational costs, this step is typically omitted in ligand docking simulations. In this study, we introduce a resource-efficient, Rosetta-based protocol named “PlaceWaters” to predict the location of explicit interface bridging waters during a ligand docking simulation. In contrast to other explicit water methods, this protocol is independent of knowledge of number and location of crystallographic waters in homologous structures. We test this method on a diverse protein-small molecule benchmark set in comparison to other Rosetta-based protocols. Our results suggest that this coarse-grained, structure-based approach quickly and accurately predicts the location of bridging waters, improving our ability to computationally screen drug candidates.

Abstract Background Recent advances in DNA sequencing technologies have enabled significant leaps in capacity to generate large volumes of DNA sequence data, which has spurred a rapid growth in the use of bioinformatics as a means of interrogating antibody variable gene repertoires. Common tools used for annotation of antibody sequences are often limited in functionality, modularity and usability. Results We have developed PyIR, a Python wrapper and library for IgBLAST, which offers a minimal setup CLI and API, FASTQ support, file chunking for large sequence files, JSON and Python dictionary output, and built-in sequence filtering. Conclusions PyIR offers improved processing speed over multithreaded IgBLAST (version 1.14) when spawning more than 16 processes on a single computer system. Its customizable filtering and data encapsulation allow it to be adapted to a wide range of computing environments. The API allows for IgBLAST to be used in customized bioinformatics workflows.

Abstract Background Advances in next-generation sequencing (NGS) of antibody repertoires have led to an explosion in B cell receptor sequence data from donors with many different disease states. These data have the potential to detect patterns of immune response across populations. However, to this point it has been difficult to interpret such patterns of immune response between disease states in the absence of functional data. There is a need for a robust method that can be used to distinguish general patterns of immune responses at the antibody repertoire level. Results We developed a method for reducing the complexity of antibody repertoire datasets using principal component analysis (PCA) and refer to our method as “repertoire fingerprinting.” We reduce the high dimensional space of an antibody repertoire to just two principal components that explain the majority of variation in those repertoires. We show that repertoires from individuals with a common experience or disease state can be clustered by their repertoire fingerprints to identify common antibody responses. Conclusions Our repertoire fingerprinting method for distinguishing immune repertoires has implications for characterizing an individual disease state. Methods to distinguish disease states based on pattern recognition in the adaptive immune response could be used to develop biomarkers with diagnostic or prognostic utility in patient care. Extending our analysis to larger cohorts of patients in the future should permit us to define more precisely those characteristics of the immune response that result from natural infection or autoimmunity.

The use of unnatural fluorogenic molecules widely expands the pallet of available genetically encoded fluorescent imaging tools through the design of fluorogen activating proteins (FAPs). While there is already a handful of such probes available, each of them went through laborious cycles of in vitro screening and selection. Computational modeling approaches are evolving incredibly fast right now and are demonstrating great results in many applications, including de novo protein design. It suggests that the easier task of fine-tuning the fluorogen-binding properties of an already functional protein in silico should be readily achievable. To test this hypothesis, we used Rosetta for computational ligand docking followed by protein binding pocket redesign to further improve the previously described FAP DiB1 that is capable of binding to a BODIPY-like dye M739. Despite an inaccurate initial docking of the chromophore, the incorporated mutations nevertheless improved multiple photophysical parameters as well as the overall performance of the tag. The designed protein, DiB-RM, shows higher brightness, localization precision, and apparent photostability in protein-PAINT super-resolution imaging compared to its parental variant DiB1. Moreover, DiB-RM can be cleaved to obtain an efficient split system with enhanced performance compared to a parental DiB-split system. The possible reasons for the inaccurate ligand binding pose prediction and its consequence on the outcome of the design experiment are further discussed. Author summary Computational approaches have recently made significant progress in the protein engineering field evolving from a tool for helping experimentalists to prioritize or short-list mutations for testing to being capable of making fully reliable predictions. However, not all the fields of protein modeling are evolving at a similar pace. That is why evaluating the capabilities of computational tools on different tasks is important to provide other scientists with up-to-date information on the state of the field. Here we tested the performance of Rosetta (one of the leading macromolecule modeling tools) in improving small molecule-binding proteins. We successfully redesigned a fluorogen binding protein DiB1 –a protein that binds a non-fluorescent molecule and enforces its fluorescence in the obtained complex–for improved brightness and better performance in super-resolution imaging. Our results suggest that such tasks can be already achieved without laborious library screenings. However, the flexibility of the proteins might still be underestimated during standard modeling protocols and should be closely evaluated.

We describe an approach for integrating distance restraints from Double Electron-Electron Resonance (DEER) spectroscopy into Rosetta with the purpose of modeling alternative protein conformations from an initial experimental structure. Fundamental to this approach is a multilateration algorithm that harnesses sets of interconnected spin label pairs to identify optimal rotamer ensembles at each residue that fit the DEER decay in the time domain. Benchmarked relative to data analysis packages, the algorithm yields comparable distance distributions with the advantage that fitting the DEER decay and rotamer ensemble optimization are coupled. We demonstrate this approach by modeling the protonation-dependent transition of the multidrug transporter PfMATE to an inward facing conformation with a deviation to the experimental structure of less than 2Å Cα RMSD. By decreasing spin label rotamer entropy, this approach engenders more accurate Rosetta models that are also more closely clustered, thus setting the stage for more robust modeling of protein conformational changes.

Parkinson disease (PD) is a progressive, neurodegenerative disorder affecting over 6.1 million people worldwide. Although the cause of PD remains unclear, studies of highly penetrant mutations identified in early-onset familial parkinsonism have contributed to our understanding of the molecular mechanisms underlying disease pathology. Dopamine (DA) transporter (DAT) deficiency syndrome (DTDS) is a distinct type of infantile parkinsonism-dystonia that shares key clinical features with PD, including motor deficits (progressive bradykinesia, tremor, hypomimia) and altered DA neurotransmission. Here, we define structural, functional, and behavioral consequences of a Cys substitution at R445 in human DAT (hDAT R445C), identified in a patient with DTDS. We found that this R445 substitution disrupts a phylogenetically conserved intracellular (IC) network of interactions that compromise the hDAT IC gate. This is demonstrated by both Rosetta molecular modeling and fine-grained simulations using hDAT R445C, as well as EPR analysis and X-ray crystallography of the bacterial homolog leucine transporter. Notably, the disruption of this IC network of interactions supported a channel-like intermediate of hDAT and compromised hDAT function. We demonstrate that Drosophila melanogaster expressing hDAT R445C show impaired hDAT activity, which is associated with DA dysfunction in isolated brains and with abnormal behaviors monitored at high-speed time resolution. We show that hDAT R445C Drosophila exhibit motor deficits, lack of motor coordination (i.e. flight coordination) and phenotypic heterogeneity in these behaviors that is typically associated with DTDS and PD. These behaviors are linked with altered dopaminergic signaling stemming from loss of DA neurons and decreased DA availability. We rescued flight coordination with chloroquine, a lysosomal inhibitor that enhanced DAT expression in a heterologous expression system. Together, these studies shed some light on how a DTDS-linked DAT mutation underlies DA dysfunction and, possibly, clinical phenotypes shared by DTDS and PD.

Protein-protein interaction (PPI) is an essential mechanism by which proteins perform their biological functions. For globular proteins, the molecular characteristics of such interactions have been well analyzed, and many computational tools are available for predicting PPI sites and constructing structural models of the complex. In contrast, little is known about the molecular features of the interaction between integral membrane proteins (IMPs) and few methods exist for constructing structural models of their complexes. Here, we analyze the interfaces from a non-redundant set of complexes of α-helical IMPs whose structures have been determined to a high resolution. We find that the interface is not significantly different from the rest of the surface in terms of average hydrophobicity. However, the interface is significantly better conserved and, on average, inter-subunit contacting residue pairs correlate more strongly than non-contacting pairs, especially in obligate complexes. We also develop a neural network-based method, with an area under the receiver operating characteristic curve of 0.75 and a Pearson correlation coefficient of 0.70, for predicting interface residues and their weighted contact numbers (WCNs). We further show that predicted interface residues and their WCNs can be used as restraints to reconstruct the structure α-helical IMP dimers through docking for fourteen out of a benchmark set of sixteen complexes. The RMSD100 values of the best-docked ligand subunit to its native structure are

Rare variants in the cardiac potassium channel KV7.1 (KCNQ1) and sodium channel NaV1.5 (SCN5A) are implicated in genetic disorders of heart rhythm, including congenital long QT and Brugada syndromes (LQTS, BrS), but also occur in reference populations. We previously reported two sets of NaV1.5 (n = 356) and KV7.1 (n = 144) variants with in vitro characterized channel currents gathered from the literature. Here we investigated the ability to predict commonly reported NaV1.5 and KV7.1 variant functional perturbations by leveraging diverse features including variant classifiers PROVEAN, PolyPhen-2, and SIFT; evolutionary rate and BLAST position specific scoring matrices (PSSM); and structure-based features including “functional densities” which is a measure of the density of pathogenic variants near the residue of interest. Structure-based functional densities were the most significant features for predicting NaV1.5 peak current (adj. R2 = 0.27) and KV7.1 + KCNE1 half-maximal voltage of activation (adj. R2 = 0.29). Additionally, use of structure-based functional density values improves loss-of-function classification of SCN5A variants with an ROC-AUC of 0.78 compared with other predictive classifiers (AUC = 0.69; two-sided DeLong test p = .01). These results suggest structural data can inform predictions of the effect of uncharacterized SCN5A and KCNQ1 variants to provide a deeper understanding of their burden on carriers. Keywords: SCN5A, KCNQ1, Function prediction, Protein structure, And protein function

Amphipathic helices have hydrophobic and hydrophilic/charged residues situated on opposite faces of the helix. They can anchor peripheral membrane proteins to the membrane, be attached to integral membrane proteins, or exist as independent peptides. Despite the widespread presence of membrane-interacting amphipathic helices, there is no computational tool within Rosetta to model their interactions with membranes. In order to address this need, we developed the AmphiScan protocol with PyRosetta, which runs a grid search to find the most favorable position of an amphipathic helix with respect to the membrane. The performance of the algorithm was tested in benchmarks with the RosettaMembrane, ref2015_memb, and franklin2019 score functions on six engineered and 44 naturally-occurring amphipathic helices using membrane coordinates from the OPM and PDBTM databases, OREMPRO server, and MD simulations for comparison. The AmphiScan protocol predicted the coordinates of amphipathic helices within less than 3Å of the reference structures and identified membrane-embedded residues with a Matthews Correlation Constant (MCC) of up to 0.57. Overall, AmphiScan stands as fast, accurate, and highly-customizable protocol that can be pipelined with other Rosetta and Python applications.

Computational protein design has the ambitious goal of crafting novel proteins that address challenges in biology and medicine. To overcome these challenges, the computational protein modeling suite Rosetta has been tailored to address various protein design tasks. Recently, statistical methods have been developed that identify correlated mutations between residues in a multiple sequence alignment of homologous proteins. These subtle inter-dependencies in the occupancy of residue positions throughout evolution are crucial for protein function, but we found that three current Rosetta design approaches fail to recover these co-evolutionary couplings. Thus, we developed the Rosetta method ResCue (residue-coupling enhanced) that leverages co-evolutionary information to favor sequences which recapitulate correlated mutations, as observed in nature. To assess the protocols via recapitulation designs, we compiled a benchmark of ten proteins each represented by two, structurally diverse states. We could demonstrate that ResCue designed sequences with an average sequence recovery rate of 70%, whereas three other protocols reached not more than 50%, on average. Our approach had higher recovery rates also for functionally important residues, which were studied in detail. This improvement has only a minor negative effect on the fitness of the designed sequences as assessed by Rosetta energy. In conclusion, our findings support the idea that informing protocols with co-evolutionary signals helps to design stable and native-like proteins that are compatible with the different conformational states required for a complex function.

The function of the voltage-gated KCNQ1 potassium channel is regulated by co-assembly with KCNE auxiliary subunits. KCNQ1-KCNE1 channels generate the slow delayed rectifier current, IKs, which contributes to the repolarization phase of the cardiac action potential. A three amino acid motif (F57-T58-L59, FTL) in KCNE1 is essential for slow activation of KCNQ1-KCNE1 channels. However, how this motif interacts with KCNQ1 to control its function is unknown. Combining computational modeling with electrophysiological studies, we developed structural models of the KCNQ1-KCNE1 complex that suggest how KCNE1 controls KCNQ1 activation. The FTL motif binds at a cleft between the voltage-sensing and pore domains and appears to affect the channel gate by an allosteric mechanism. Comparison with the KCNQ1-KCNE3 channel structure suggests a common transmembrane-binding mode for different KCNEs and illuminates how specific differences in the interaction of their triplet motifs determine the profound differences in KCNQ1 functional modulation by KCNE1 versus KCNE3.

As sequencing methodologies continue to advance, the availability of protein sequences far outpaces the ability of structure determination. Homology modeling is used to bridge this gap but relies on high-identity templates for accurate model building. G-protein coupled receptors (GPCRs) represent a significant target class for pharmaceutical therapies in which homology modeling could fill the knowledge gap for structure-based drug design. To date, only about 17% of druggable GPCRs have had their structures characterized at atomic resolution. However, modeling of the remaining 83% is hindered by the low sequence identity between receptors. Here we test key inputs in the model building process using GPCRs as a focus to improve the pipeline in two critical ways: Firstly, we use a blended sequence- and structure-based alignment that accounts for structure conservation in loop regions. Secondly, by merging multiple template structures into one comparative model, the best possible template for every region of a target can be used expanding the conformational space sampled in a meaningful way. This optimization allows for accurate modeling of receptors using templates as low as 20% sequence identity, which accounts for nearly the entire druggable space of GPCRs. A model database of all non-odorant GPCRs is made available at www.rosettagpcr.org. Additionally, all protocols are made available with insights into modifications that may improve accuracy at new targets.

Cystic fibrosis (CF) is a rare genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial anion channel expressed in several vital organs. Absence of functional CFTR results in imbalanced osmotic equilibrium and subsequent mucus build up in the lungs-which increases the risk of infection and eventually causes death. CFTR is an ATP-binding cassette (ABC) transporter family protein composed of two transmembrane domains (TMDs), two nucleotide binding domains (NBDs), and an unstructured regulatory domain. The most prevalent patient mutation is the deletion of F508 (F508del), making F508del CFTR the primary target for current FDA approved CF therapies. However, no experimental multi-domain F508del CFTR structure has been determined and few studies have modeled F508del using multi-domain WT CFTR structures. Here, we used cryo-EM density data and Rosetta comparative modeling (RosettaCM) to compare a F508del model with published experimental data on CFTR NBD1 thermodynamics. We then apply this modeling method to generate multi-domain WT and F508del CFTR structural models. These models demonstrate the destabilizing effects of F508del on NBD1 and the NBD1/TMD interface in both the inactive and active conformation of CFTR. Furthermore, we modeled F508del/R1070W and F508del bound to the CFTR corrector VX-809. Our models reveal the stabilizing effects of VX-809 on multi-domain models of F508del CFTR and pave the way for rational design of additional drugs that target F508del CFTR for treatment of CF.

The cardiac sodium ion channel (NaV1.5) is a protein with four domains (DI-DIV), each with six transmembrane segments. Its opening and subsequent inactivation results in the brief rapid influx of Na+ ions resulting in the depolarization of cardiomyocytes. The neurotoxin veratridine (VTD) inhibits NaV1.5 inactivation resulting in longer channel opening times, and potentially fatal action potential prolongation. VTD is predicted to bind at the channel pore, but alternative binding sites have not been ruled out. To determine the binding site of VTD on NaV1.5, we perform docking calculations and high-throughput electrophysiology experiments in the present study. The docking calculations identified two distinct binding regions. The first site was in the pore, close to the binding site of NaV1.4 and NaV1.5 blocking drugs in experimental structures. The second site was at the “mouth” of the pore at the cytosolic side, partly solvent-exposed. Mutations at this site (L409, E417, and I1466) had large effects on VTD binding, while residues deeper in the pore had no effect, consistent with VTD binding at the mouth site. Overall, our results suggest a VTD binding site close to the cytoplasmic mouth of the channel pore. Binding at this alternative site might indicate an allosteric inactivation mechanism for VTD at NaV1.5.

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.

The cardiac action potential is critical to the production of a synchronized heartbeat. This electrical impulse is governed by the intricate activity of cardiac ion channels, among them the cardiac voltage-gated potassium (Kv) channels KCNQ1 and hERG as well as the voltage-gated sodium (Nav) channel encoded by SCN5A. Each channel performs a highly distinct function, despite sharing a common topology and structural components. These three channels are also the primary proteins mutated in congenital long QT syndrome (LQTS), a genetic condition that predisposes to cardiac arrhythmia and sudden cardiac death due to impaired repolarization of the action potential and has a particular proclivity for reentrant ventricular arrhythmias. Recent cryo-electron microscopy structures of human KCNQ1 and hERG, along with the rat homolog of SCN5A and other mammalian sodium channels, provide atomic-level insight into the structure and function of these proteins that advance our understanding of their distinct functions in the cardiac action potential, as well as the molecular basis of LQTS. In this review, the gating, regulation, LQTS mechanisms, and pharmacological properties of KCNQ1, hERG, and SCN5A are discussed in light of these recent structural findings.

Computational protein design of an ensemble of conformations for one protein-i.e., multi-state design-determines the side chain identity by optimizing the energetic contributions of that side chain in each of the backbone conformations. Sampling the resulting large sequence-structure search space limits the number of conformations and the size of proteins in multi-state design algorithms. Here, we demonstrated that the REstrained CONvergence (RECON) algorithm can simultaneously evaluate the sequence of large proteins that undergo substantial conformational changes. Simultaneous optimization of side chain conformations across all conformations increased sequence conservation when compared to single-state designs in all cases. More importantly, the sequence space sampled by RECON MSD resembled the evolutionary sequence space of flexible proteins, particularly when confined to predicting the mutational preferences of limited common ancestral descent, such as in the case of influenza type A hemagglutinin. Additionally, we found that sequence positions which require substantial changes in their local environment across an ensemble of conformations are more likely to be conserved. These increased conservation rates are better captured by RECON MSD over multiple conformations and thus multiple local residue environments during design. To quantify this rewiring of contacts at a certain position in sequence and structure, we introduced a new metric designated 'contact proximity deviation' that enumerates contact map changes. This measure allows mapping of global conformational changes into local side chain proximity adjustments, a property not captured by traditional global similarity metrics such as RMSD or local similarity metrics such as changes in φ and ψ angles.

Voltage-gated ion channels feature voltage sensor domains (VSDs) that exist in three distinct conformations during activation: resting, intermediate, and activated. Experimental determination of the structure of a potassium channel VSD in the intermediate state has previously proven elusive. Here, we report and validate the experimental three-dimensional structure of the human KCNQ1 voltage-gated potassium channel VSD in the intermediate state. We also used mutagenesis and electrophysiology in Xenopus laevisoocytes to functionally map the determinants of S4 helix motion during voltage-dependent transition from the intermediate to the activated state. Finally, the physiological relevance of the intermediate state KCNQ1 conductance is demonstrated using voltage-clamp fluorometry. This work illuminates the structure of the VSD intermediate state and demonstrates that intermediate state conductivity contributes to the unusual versatility of KCNQ1, which can function either as the slow delayed rectifier current (IKs) of the cardiac action potential or as a constitutively active epithelial leak current.

The antibody (Ab) germline gene rearrangement of variable (V), diversity (D), and joining (J) gene segments, as well as somatic hypermutation, give rise to the human Ab variable gene sequence repertoire. It is common to characterize single nucleotide frequencies of the variable region by alignment to species-specific wildtype germline genes. The increasing application of next-generation sequencing to immune repertoire studies has led to the compilation of increasing large adaptive immunome receptor repertoire datasets. We have developed a method that maps the sequence of a target Ab onto an immunome dataset of 326 million human Ab sequences. For this purpose, we created a position- and gene-specific scoring matrix (PGSSM) and its corresponding antibody similarity score. We characterized our PGSSM score and found that it strongly correlated with the phylogenetic distance of 181,355 Ab sequences from GenBank across 20 species. The most likely human nucleotide back-translation was obtained given only PGSSMs and the amino acid sequence of an Ab achieving a nucleotide sequence recovery of 95.9% and 97.2% for human heavy and light chains, respectively. In conclusion, the scoring of our back-translation is a valuable estimate for the similarity of an Ab sequence to the natural human repertoire. As expected, Ab therapeutic molecules developed from a human source showed a higher similarity to the repertoire than engineered Abs. Thus, the PGSSM metric introduced here can be used to engineer human-like Ab therapeutics.

Rosetta is a computational software suite containing algorithms for a wide variety of macromolecular structure prediction and design tasks including small molecule protocols commonly used in drug discovery or enzyme design. Here, we benchmark RosettaLigand score functions and protocols in comparison to results of other software recently published in the Comparative Assessment of Score Functions (CASF-2016). The CASF-2016 benchmark covers a wide variety of tests including scoring and ranking multiple compounds against a target, ligand docking of a small molecule to a target, and virtual screening to extract binders from a compound library. Direct comparison to the score functions provided by CASF-2016 results shows that the original RosettaLigand score function ranks among the top software for scoring, ranking, docking and screening tests. Most notably, the RosettaLigand score function ranked 2/34 among other report score functions in CASF-2016. We additionally perform a ligand docking test with full sampling to mimic typical use cases. Despite improved performance of newer score functions in canonical protein structure prediction and design, we demonstrate here that more recent Rosetta score functions have reduced performance across all small molecule benchmarks. The tests described here have also been uploaded to the Rosetta scientific benchmarking server and will be run weekly to track performance as the code is continually being developed.

ABSTRACT Staphylococcus aureus is an important human pathogen that infects nearly every human tissue. Like most organisms, the acquisition of nutrient iron is necessary for its survival. One route by which it obtains this metal is through the iron-regulated surface determinant (Isd) system that scavenges iron from the hemoglobin of the host. We show that the heavy chain variable region IGHV1-69 gene commonly encodes human monoclonal antibodies (mAbs) targeting IsdB-NEAT2. Remarkably, these antibodies bind to multiple antigenic sites. One class of IGHV1-69-encoded mAbs blocks S. aureus heme acquisition by binding to the heme-binding site of NEAT2, while two additional classes reduce the bacterial burden in vivo by an alternative Fc receptor-mediated mechanism. We further identified clonal lineages of IGHV1-69-encoded mAbs using donor samples, showing that each lineage diversifies during infection by somatic hypermutation. These studies reveal that IGHV1-69-encoded antibodies contribute to a protective immune response, furthering our understanding of the correlates of protection against S. aureus infection. IMPORTANCE The human pathogen Staphylococcus aureus causes a wide range of infections, including skin abscesses and sepsis. There is currently no licensed vaccine to prevent S. aureus infection, and its treatment has become increasingly difficult due to antibiotic resistance. One potential way to inhibit S. aureus pathogenesis is to prevent iron acquisition. The iron-regulated surface determinant (Isd) system has evolved in S. aureus to acquire hemoglobin from the human host as a source of heme-iron. In this study, we investigated the molecular and structural basis for antibody-mediated correlates against a member of the Isd system, IsdB. The association of immunoglobulin heavy chain variable region IGHV1-69 gene-encoded human monoclonal antibodies with the response against S. aureus IsdB is described using structural and functional studies to define the importance of this antibody class. We also determine that somatic hypermutation in the development of these antibodies hinders rather than fine-tunes the immune response to IsdB.

Abstract Background Next-generation sequencing of individuals with genetic diseases often detects candidate rare variants in numerous genes, but determining which are causal remains challenging. We hypothesized that the spatial distribution of missense variants in protein structures contains information about function and pathogenicity that can help prioritize variants of unknown significance (VUS) and elucidate the structural mechanisms leading to disease. Results To illustrate this approach in a clinical application, we analyzed 13 candidate missense variants in regulator of telomere elongation helicase 1 (RTEL1) identified in patients with Familial Interstitial Pneumonia (FIP). We curated pathogenic and neutral RTEL1 variants from the literature and public databases. We then used homology modeling to construct a 3D structural model of RTEL1 and mapped known variants into this structure. We next developed a pathogenicity prediction algorithm based on proximity to known disease causing and neutral variants and evaluated its performance with leave-one-out cross-validation. We further validated our predictions with segregation analyses, telomere lengths, and mutagenesis data from the homologous XPD protein. Our algorithm for classifying RTEL1 VUS based on spatial proximity to pathogenic and neutral variation accurately distinguished 7 known pathogenic from 29 neutral variants (ROC AUC = 0.85) in the N-terminal domains of RTEL1. Pathogenic proximity scores were also significantly correlated with effects on ATPase activity (Pearson r = −0.65, p = 0.0004) in XPD, a related helicase. Applying the algorithm to 13 VUS identified from sequencing of RTEL1 from patients predicted five out of six disease-segregating VUS to be pathogenic. We provide structural hypotheses regarding how these mutations may disrupt RTEL1 ATPase and helicase function. Conclusions Spatial analysis of missense variation accurately classified candidate VUS in RTEL1 and suggests how such variants cause disease. Incorporating spatial proximity analyses into other pathogenicity prediction tools may improve accuracy for other genes and genetic diseases.

G protein-coupled receptors (GPCRs) represent the largest membrane protein family and a significant target class for therapeutics. Receptors from GPCRs’ largest class, class A, influence virtually every aspect of human physiology. About 45% of the members of this family endogenously bind flexible peptides or peptides segments within larger protein ligands. While many of these peptides have been structurally characterized in their solution state, the few studies of peptides in their receptor-bound state suggest that these peptides interact with a shared set of residues and undergo significant conformational changes. For the purpose of understanding binding dynamics and the development of peptidomimetic drug compounds, further studies should investigate the peptide ligands that are complexed to their cognate receptor.