IL11-mediated stromal cell activation may not be the master regulator of pro-fibrotic signaling downstream of TGFß.

Fibrotic diseases, such as idiopathic pulmonary fibrosis (IPF) and systemic scleroderma (SSc), are commonly associated with high morbidity and mortality, thereby representing a significant unmet medical need. Interleukin 11 (IL11)-mediated cell activation has been identified as a central mechanism for promoting fibrosis downstream of TGFß. IL11 signaling has recently been reported to promote fibroblast-to-myofibroblast transition, thus leading to various pro-fibrotic phenotypic changes. We confirmed increased mRNA expression of IL11 and IL11Rα in fibrotic diseases by OMICs approaches and in situ hybridization. However, the vital role of IL11 as a driver for fibrosis was not recapitulated. While induction of IL11 secretion was observed downstream of TGFß signaling in human lung fibroblasts and epithelial cells, the cellular responses induced by IL11 was quantitatively and qualitatively inferior to that of TGFß at the transcriptional and translational levels. IL11 blocking antibodies inhibited IL11Rα-proximal STAT3 activation but failed to block TGFß-induced profibrotic signals. In summary, our results challenge the concept of IL11 blockade as a strategy for providing transformative treatment for fibrosis.

TransMHCII: a novel MHC-II binding prediction model built using a protein language model and an image classifier.

The emergence of deep learning models such as AlphaFold2 has revolutionized the structure prediction of proteins. Nevertheless, much remains unexplored, especially on how we utilize structure models to predict biological properties. Herein, we present a method using features extracted from protein language models (PLMs) to predict the major histocompatibility complex class II (MHC-II) binding affinity of peptides. Specifically, we evaluated a novel transfer learning approach where the backbone of our model was interchanged with architectures designed for image classification tasks. Features extracted from several PLMs (ESM1b, ProtXLNet or ProtT5-XL-UniRef) were passed into image models (EfficientNet v2b0, EfficientNet v2m or ViT-16). The optimal pairing of the PLM and image classifier resulted in the final model TransMHCII, outperforming NetMHCIIpan 3.2 and NetMHCIIpan 4.0-BA on the receiver operating characteristic area under the curve, balanced accuracy and Jaccard scores. The architecture innovation may facilitate the development of other deep learning models for biological problems.

A novel method for in silico assessment of Methionine oxidation risk in monoclonal antibodies: Improvement over the 2-shell model.

Over the past decade, therapeutic monoclonal antibodies (mAbs) have established their role as valuable agents in the treatment of various diseases ranging from cancers to infectious, cardiovascular and autoimmune diseases. Reactive groups of the amino acids within these proteins make them susceptible to many kinds of chemical modifications during manufacturing, storage and in vivo circulation. Among these reactions, the oxidation of methionine residues to their sulfoxide form is a commonly observed chemical modification in mAbs. When the oxidized methionine is in the complementarity-determining region (CDR), this modification can affect antigen binding and thus abrogate biological activity. For these reasons, it is essential to identify oxidation liabilities during the antibody discovery and development phases. Here, we present an in silico method, based on protein modeling and molecular dynamics simulations, to predict the oxidation-liable residues in the variable region of therapeutic antibodies. Previous studies have used the 2-shell water coordination number descriptor (WCN) to identify methionine residues susceptible to oxidation. Although the WCN descriptor successfully predicted oxidation liabilities when the residue was solvent exposed, the method was much less accurate for partially buried methionine residues. Consequently, we introduce a new descriptor, WCN-OH, that improves the accuracy of prediction of methionine oxidation susceptibility by extending the theoretical framework of the water coordination number to incorporate the effects of polar amino acids side chains in close proximity to the methionine of interest.

Separating clinical antibodies from repertoire antibodies, a path to in silico developability assessment.

Approaches for antibody discovery have seen substantial improvement and success in recent years. Yet, advancing antibodies into the clinic remains difficult because therapeutic developability concerns are challenging to predict. We developed a computational model to simplify antibody developability assessment and enable accelerated early-stage screening. To this end, we quantified the ability of hundreds of sequence- and structure-based descriptors to differentiate clinical antibodies that have undergone rigorous screening and characterization for drug-like properties from antibodies in the human repertoire that are not natively paired. This analysis identified 144 descriptors capable of distinguishing clinical from repertoire antibodies. Five descriptors were selected and combined based on performance and orthogonality into a single model referred to as the Therapeutic Antibody Developability Analysis (TA-DA). On a hold-out test set, this tool separated clinical antibodies from repertoire antibodies with an AUC = 0.8, demonstrating the ability to identify developability attributes unique to clinical antibodies. Based on our results, the TA-DA score may serve as an approach for selecting lead antibodies for further development.Abbreviations: Affinity-Capture Self-Interaction Nanoparticle Spectroscopy (AC-SINS), Area Under the Curve (AUC), Complementary-Determining Region (CDR), Clinical-Stage Therapeutics (CST), Framework (FR), Monoclonal Antibodies (mAbs), Observed Antibody Space (OAS), Receiver Operating Characteristic (ROC), Size-Exclusion Chromatography (SEC), Structural Aggregation Propensity (SAP), Therapeutic Antibody Developability Analysis (TA-DA), Therapeutic Antibody Profiler (TAP), Therapeutic Structural Antibody Database (Thera-SAbDab), Variable Heavy (VH), Variable Light (VL).

Surface salt bridges contribute to the extreme thermal stability of an FN3-like domain from a thermophilic bacterium.

This study uses differential scanning calorimetry, X-ray crystallography, and molecular dynamics simulations to investigate the structural basis for the high thermal stability (melting temperature 97.5°C) of a FN3-like protein domain from thermophilic bacteria Thermoanaerobacter tengcongensis (FN3tt). FN3tt adopts a typical FN3 fold with a three-stranded beta sheet packing against a four-stranded beta sheet. We identified three solvent exposed arginine residues (R23, R25, and R72), which stabilize the protein through salt bridge interactions with glutamic acid residues on adjacent strands. Alanine mutation of the three arginine residues reduced melting temperature by up to 22°C. Crystal structures of the wild type (WT) and a thermally destabilized (∆Tm -19.7°C) triple mutant (R23L/R25T/R72I) were found to be nearly identical, suggesting that the destabilization is due to interactions of the arginine residues. Molecular dynamics simulations showed that the salt bridge interactions in the WT were stable and provided a dynamical explanation for the cooperativity observed between R23 and R25 based on calorimetry measurements. In addition, folding free energy changes computed using free energy perturbation molecular dynamics simulations showed high correlation with melting temperature changes. This work is another example of surface salt bridges contributing to the enhanced thermal stability of thermophilic proteins. The molecular dynamics simulation methods employed in this study may be broadly useful for in silico surface charge engineering of proteins.

Large-scale in vitro functional testing and novel variant scoring via protein modeling provide insights into alkaline phosphatase activity in hypophosphatasia.

Hypophosphatasia (HPP) is a rare metabolic disorder characterized by low tissue-nonspecific alkaline phosphatase (TNSALP) typically caused by ALPL gene mutations. HPP is heterogeneous, with clinical presentation correlating with residual TNSALP activity and/or dominant-negative effects (DNE). We measured residual activity and DNE for 155 ALPL variants by transient transfection and TNSALP enzymatic activity measurement. Ninety variants showed low residual activity and 24 showed DNE. These results encompass all missense variants with carrier frequencies above 1/25,000 from the Genome Aggregation Database. We used resulting data as a reference to develop a new computational algorithm that scores ALPL missense variants and predicts high/low TNSALP enzymatic activity. Our approach measures the effects of amino acid changes on TNSALP dimer stability with a physics-based implicit solvent energy model. We predict mutation deleteriousness with high specificity, achieving a true-positive rate of 0.63 with false-positive rate of 0, with an area under receiver operating curve (AUC) of 0.9, better than all in silico predictors tested. Combining this algorithm with other in silico approaches can further increase performance, reaching an AUC of 0.94. This study expands our understanding of HPP heterogeneity and genotype/phenotype relationships with the aim of improving clinical ALPL variant interpretation.

Predicting mutations deleterious to function in beta-lactamase TEM1 using MM-GBSA.

Missense mutations can have disastrous effects on the function of a protein. And as a result, they have been implicated in numerous diseases. However, the majority of missense variants only have a nominal impact on protein function. Thus, the ability to distinguish these two classes of missense mutations would greatly aid drug discovery efforts in target identification and validation as well as medical diagnosis. Monitoring the co-occurrence of a given missense mutation and a disease phenotype provides a pathway for classifying functionally disrupting missense mutations. But, the occurrence of a specific missense variant is often extremely rare making statistical links challenging to infer. In this study, we benchmark a physics-based approach for predicting changes in stability, MM-GBSA, and apply it to classifying mutations as functionally disrupting. A large and diverse dataset of 990 residue mutations in beta-lactamase TEM1 is used to assess performance as it is rich in both functionally disrupting mutations and functionally neutral/beneficial mutations. On this dataset, we compare the performance of MM-GBSA to alternative strategies for predicting functionally disrupting mutations. We observe that the MM-GBSA method obtains an area under the curve (AUC) of 0.75 on the entire dataset, outperforming all other predictors tested. More importantly, MM-GBSA's performance is robust to various divisions of the dataset, speaking to the generality of the approach. Though there is one notable exception: Mutations on the surface of the protein are the mutations that are the most difficult to classify as functionally disrupting for all methods tested. This is likely due to the many mechanisms available to surface mutations to disrupt function, and thus provides a direction of focus for future studies.

Relative Binding Affinity Prediction of Charge-Changing Sequence Mutations with FEP in Protein-Protein Interfaces.

Building on the substantial progress that has been made in using free energy perturbation (FEP) methods to predict the relative binding affinities of small molecule ligands to proteins, we have previously shown that results of similar quality can be obtained in predicting the effect of mutations on the binding affinity of protein-protein complexes. However, these results were restricted to mutations which did not change the net charge of the side chains due to known difficulties with modeling perturbations involving a change in charge in FEP. Various methods have been proposed to address this problem. Here we apply the co-alchemical water approach to study the efficacy of FEP calculations of charge changing mutations at the protein-protein interface for the antibody-gp120 system investigated previously and three additional complexes. We achieve an overall root mean square error of 1.2 kcal/mol on a set of 106 cases involving a change in net charge selected by a simple suitability filter using side-chain predictions and solvent accessible surface area to be relevant to a biologic optimization project. Reasonable, although less precise, results are also obtained for the 44 more challenging mutations that involve buried residues, which may in some cases require substantial reorganization of the local protein structure, which can extend beyond the scope of a typical FEP simulation. We believe that the proposed prediction protocol will be of sufficient efficiency and accuracy to guide protein engineering projects for which optimization and/or maintenance of a high degree of binding affinity is a key objective.

Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations.

The therapeutic effect of targeted kinase inhibitors can be significantly reduced by intrinsic or acquired resistance mutations that modulate the affinity of the drug for the kinase. In cancer, the majority of missense mutations are rare, making it difficult to predict their impact on inhibitor affinity. This complicates the practice of precision medicine, pairing of patients with clinical trials, and development of next-generation inhibitors. Here, we examine the potential for alchemical free-energy calculations to predict how kinase mutations modulate inhibitor affinities to Abl, a major target in chronic myelogenous leukemia (CML). We find these calculations can achieve useful accuracy in predicting resistance for a set of eight FDA-approved kinase inhibitors across 144 clinically-identified point mutations, achieving a root mean square error in binding free energy changes of 1.10.91.3 kcal/mol (95% confidence interval) and correctly classifying mutations as resistant or susceptible with 888293% accuracy. Since these calculations are fast on modern GPUs, this benchmark establishes the potential for physical modeling to collaboratively support the rapid assessment and anticipation of the potential for patient mutations to affect drug potency in clinical applications.

Predicting the Effect of Amino Acid Single-Point Mutations on Protein Stability-Large-Scale Validation of MD-Based Relative Free Energy Calculations.

The stability of folded proteins is critical to their biological function and for the efficacy of protein therapeutics. Predicting the energetic effects of protein mutations can improve our fundamental understanding of structural biology, the molecular basis of diseases, and possible routes to addressing those diseases with biological drugs. Identifying the effect of single amino acid point mutations on the thermodynamic equilibrium between the folded and unfolded states of a protein can pinpoint residues of critical importance that should be avoided in the process of improving other properties (affinity, solubility, viscosity, etc.) and suggest changes at other positions for increasing stability in protein engineering. Multiple computational tools have been developed for in silico predictions of protein stability in recent years, ranging from sequence-based empirical approaches to rigorous physics-based free energy methods. In this work, we show that FEP+, which is a free energy perturbation method based on all-atom molecular dynamics simulations, can provide accurate thermal stability predictions for a wide range of biologically relevant systems. Significantly, the FEP+ approach, while originally developed for relative binding free energies of small molecules to proteins and not specifically fitted for protein stability calculations, performs well compared to other methods that were fitted specifically to predict protein stability. Here, we present the broadest validation of a rigorous free energy-based approach applied to protein stability reported to date: 700+ single-point mutations spanning 10 different protein targets. Across the entire data set, we correctly classify the mutations as stabilizing or destabilizing in 84% of the cases, and obtain statistically significant predictions as compared with experiment [average error of ~1.6kcal/mol and coefficient of determination (R2) of 0.40]. This study demonstrates, for the first time in a large-scale validation, that rigorous free energy calculations can be used to predict changes in protein stability from point mutations without parameterization or system-specific customization, although further improvements should be possible with additional sampling and a better representation of the unfolded state of the protein. Here, we describe the FEP+ method as applied to protein stability calculations, summarize the large-scale retrospective validation results, and discuss limitations of the method, along with future directions for further improvements.

A set of computationally designed orthogonal antiparallel homodimers that expands the synthetic coiled-coil toolkit.

Molecular engineering of protein assemblies, including the fabrication of nanostructures and synthetic signaling pathways, relies on the availability of modular parts that can be combined to give different structures and functions. Currently, a limited number of well-characterized protein interaction components are available. Coiled-coil interaction modules have been demonstrated to be useful for biomolecular design, and many parallel homodimers and heterodimers are available in the coiled-coil toolkit. In this work, we sought to design a set of orthogonal antiparallel homodimeric coiled coils using a computational approach. There are very few antiparallel homodimers described in the literature, and none have been measured for cross-reactivity. We tested the ability of the distance-dependent statistical potential DFIRE to predict orientation preferences for coiled-coil dimers of known structure. The DFIRE model was then combined with the CLASSY multistate protein design framework to engineer sets of three orthogonal antiparallel homodimeric coiled coils. Experimental measurements confirmed the successful design of three peptides that preferentially formed antiparallel homodimers that, furthermore, did not interact with one additional previously reported antiparallel homodimer. Two designed peptides that formed higher-order structures suggest how future design protocols could be improved. The successful designs represent a significant expansion of the existing protein-interaction toolbox for molecular engineers.

Multistate protein design using CLEVER and CLASSY.

Structure-based protein design is a powerful technique with great potential. Challenges in two areas limit performance: structure scoring and sequence-structure searching. Many of the functions used to describe the relationship between protein sequence and energy are computationally expensive to evaluate, and the spaces that must be searched in protein design are enormous. Here, we describe computational tools that can be used in certain situations to provide enormous accelerations in protein design. Cluster expansion is a technique that maps a complex function of three-dimensional atomic coordinates to a simple function of sequence. This is done by expanding the sequence-energy relation as a linear function of sequence variables, which are fit using training examples. Generating a simpler function speeds up scoring dramatically, relative to all-atom methods, and facilitates the use of new types of search strategies. The application of cluster expansion in protein modeling is new but has shown utility for design problems that require simultaneous consideration of multiple states. In this chapter, we describe cases where cluster expansion can be useful, outline how to generate a cluster-expanded version of any existing scoring procedure using the software CLEVER, and describe how to apply a cluster-expanded potential to multistate protein design using the CLASSY method.

Manipulating cofactor binding thermodynamics in an artificial oxygen transport protein.

We report the mutational analysis of an artificial oxygen transport protein, HP7, which operates via a mechanism akin to that of human neuroglobin and cytoglobin. This protein destabilizes one of two heme-ligating histidine residues by coupling histidine side chain ligation with the burial of three charged glutamate residues on the same helix. Replacement of these glutamate residues with alanine, which is uncharged, increases the affinity of the distal histidine ligand by a factor of 13. Paradoxically, it also decreases heme binding affinity by a factor of 5 in the reduced state and 60 in the oxidized state. Application of a three-state binding model, in which an initial pentacoordinate binding event is followed by a protein conformational change to hexacoordinate, provides insight into the mechanism of this seemingly counterintuitive result: the initial pentacoordinate encounter complex is significantly destabilized by the loss of the glutamate side chains, and the increased affinity for the distal histidine only partially compensates for that. These results point to the importance of considering each oxidation and conformational state in the design of functional artificial proteins.

Design principles for chlorophyll-binding sites in helical proteins.

The cyclic tetrapyrroles, viz. chlorophylls (Chl), their bacterial analogs bacteriochlorophylls, and hemes are ubiquitous cofactors of biological catalysis that are involved in a multitude of reactions. One systematic approach for understanding how Nature achieves functional diversity with only this handful of cofactors is by designing de novo simple and robust protein scaffolds with heme and/or (bacterio)chlorophyll [(B)Chls]-binding sites. This strategy is currently mostly implemented for heme-binding proteins. To gain more insight into the factors that determine heme-/(B)Chl-binding selectivity, we explored the geometric parameters of (B)Chl-binding sites in a nonredundant subset of natural (B)Chl protein structures. Comparing our analysis to the study of a nonredundant database of heme-binding helical histidines by Negron et al. (Proteins 2009;74:400-416), we found a preference for the m-rotamer in (B)Chl-binding helical histidines, in contrast to the preferred t-rotamer in heme-binding helical histidines. This may be used for the design of specific heme- or (B)Chl-binding sites in water-soluble helical bundles, because the rotamer type defines the positioning of the bound cofactor with respect to the helix interface and thus the protein-binding site. Consensus sequences for (B)Chl binding were identified by combining a computational and database-derived approach and shown to be significantly different from the consensus sequences recommended by Negron et al. (Proteins 2009;74:400-416) for heme-binding helical proteins. The insights gained in this work on helix- (B)Chls-binding pockets provide useful guidelines for the construction of reasonable (B)Chl-binding protein templates that can be optimized by computational tools.

Geometric constraints for porphyrin binding in helical protein binding sites.

Helical bundles which bind heme and porphyrin cofactors have been popular targets for cofactor-containing de novo protein design. By analyzing a highly nonredundant subset of the protein databank we have determined a rotamer distribution for helical histidines bound to heme cofactors. Analysis of the entire nonredundant database for helical sequence preferences near the ligand histidine demonstrated little preference for amino acid side chain identity, size, or charge. Analysis of the database subdivided by ligand histidine rotamer, however, reveals strong preferences in each case, and computational modeling illuminates the structural basis for some of these findings. The majority of the rotamer distribution matches that predicted by molecular simulation of a single porphyrin-bound histidine residue placed in the center of an all-alanine helix, and the deviations explain two prominent features of natural heme protein binding sites: heme distortion in the case of the cytochromes C in the m166 histidine rotamer, and a highly prevalent glycine residue in the t73 histidine rotamer. These preferences permit derivation of helical consensus sequence templates which predict optimal side chain-cofactor packing interactions for each rotamer. These findings thus promise to guide future design endeavors not only in the creation of higher affinity heme and porphyrin binding sites, but also in the direction of bound cofactor geometry.