Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation

Design of orthogonal constant domain interfaces to aid proper heavy/light chain pairing of bispecific antibodies

The correct pairing of cognate heavy and light chains is critical to the efficient manufacturing of IgG-like bispecific antibodies (bsAbs) from a single host cell. We present a general solution for the elimination of heavy chain (HC):light chain (LC) mispairs in bsAbs with κ LCs via the use of two orthogonal constant domain (CH1:Cκ ) interfaces comprising computationally designed amino acid substitutions. Substitutions were designed by Rosetta to introduce novel hydrogen bond (H-bond) networks at the CH1:Cκ interface, followed by Rosetta energy calculations to identify designs with enhanced pairing specificity and interface stability. Our final design, featuring a total of 11 amino acid substitutions across two Fab constant regions, was tested on a set of six IgG-like bsAbs featuring a diverse set of unmodified human antibody variable domains. Purity assessments showed near-complete elimination of LC mispairs, including in cases with high baseline mispairing with wild-type constant domains. The engineered bsAbs broadly recapitulated the antigen-binding and biophysical developability properties of their monospecific counterparts and no adverse immunogenicity signal was identified by an in vitro assay. Fab crystal structures containing engineered constant domain interfaces revealed no major perturbations relative to the wild-type coordinates and validated the presence of the designed hydrogen bond interactions. Our work enables the facile assembly of independently discovered IgG-like bispecific antibodies in a single-cell host and demonstrates a streamlined and generalizable computational and experimental workflow for redesigning conserved protein:protein interfaces.

Mitigating PCOS progression: The protective effect of C-phycocyanin on ovarian granulosa cell pyroptosis via the NRF2/NLRP3/GSDMD pathway

BACKGROUND

Pyroptosis is a proinflammatory cell death process that contributes to inflammatory diseases. C-Phycocyanin (C-PC) is a water-soluble protein pigment primarily derived from cyanobacteria, and it exhibits anti-inflammatory effects. However, the role of natural C-PC in pyroptosis in human ovarian granulosa cells (GCs), particularly in conditions like polycystic ovary syndrome (PCOS), remains unclear. This study investigates the effects of C-PC on pyroptosis and its relevance to PCOS.

METHODS

Here dehydroepiandrosterone (DHEA) was used to induce PCOS in a mouse model that was characterized by an irregular oestrous cycle, cystic follicles and an elevated serum hormone level. DHEA treatment was used to induce GC pyroptosis. Scanning electron microscopy (SEM) was used to determine cell morphology. The expression levels of key proteins for oxidative stress and pyroptosis, including nuclear factor erythroid 2-related factor 2 (NRF2), NLR family of pyrroline-containing structural domain 3 (NLRP3) inflammasomes, cleaved caspase-1, and N-GSDMD were investigated in vivo and in vitro by Western blotting and immunofluorescence staining.

RESULTS

C-PC restored the estrous cycle in PCOS mice, reduced testosterone levels, and decreased cystic follicles. It attenuated PCOS progression by reducing oxidative stress level and suppressing GCs pyroptosis. At the cellular level, C-PC inhibited DHEA-induced GC pyroptosis by blocking NLRP3 inflammasome activation and lowering ROS, effects reversed by the NRF2 inhibitor (ML385). Molecular docking analysis and CETSA suggested that C-PC may protect against PCOS by activating the NRF2 pathway or by indirectly binding to the Ser27 site of GSDMD. In addition, C-PC suppressed ROS/p38-MAPK activation induced by DHEA, and p38-MAPK agonists diminished its effect on NLRP3 inflammasome activity and GC pyroptosis protection.

CONCLUSION

C-PC exerts a protective effect on GC pyroptosis through the NRF2/NLRP3/GSDMD and ROS/p-38 MAPK pathways, highlighting its potential to mitigate inflammation and its relevance to reproductive health issues like PCOS.

Construction of safer hirudin-derived peptides with enhanced anticoagulant properties based on C-terminal active residue adaptation and modification

INTRODUCTION

Hirudin exerts anticoagulant effects by inhibiting the binding and catalytic activity of thrombin to fibrinogen. However, its rigid N-terminal region irreversibly occupies the thrombin catalytic center, raising concerns about potential bleeding.

OBJECTIVES

In this study, a novel lead compound, WPHVC_V1, which is based on the competitive binding mechanism of the hirudin variant WPHV_C, was developed and validated for in vitro and in vivo activity and safety.

METHODS

Saturation mutagenesis, molecular dynamics simulations and mutant protein activity assays were used to elucidate the competitive anticoagulant mechanism between WPHV_C and thrombin. Next, a recombinantly expressed tyrosylprotein sulfotransferase was used to modify and confirm the sulfation site on the C-terminal tyrosine of hirudin. Finally, a multisite aromatic amino acid mutation strategy was implemented to design and synthesize the lead anticoagulant, WPHVC_V1.

RESULTS

The acidic amino acid cluster in WPHV_C formed strong electrostatic interactions with the positively charged thrombin exosite I, blocking fibrinogen binding. The introduction of aromatic amino acids further stabilized the complex through π-π stacking and π-cation interactions. For example, mutation of 13E to A decreased the free energy of dissociation (ΔG) from 19.27 to 10.93 kcal·mol-1 and shortened the thrombin time (TT) from 42.00 s to 30.94 s, whereas mutation of 26 K to W increased the ΔG to 24.70 kcal·mol-1 and prolonged TT to 51.92 s. In addition, the aromatic effect of 20Y, combined with sulfation, synergistically enhanced binding. Based on these findings, the newly designed WPHVC_V1 showed a ΔG of 37.24 kcal·mol-1 and, at 0.1 mg/ml, increased TT/APTT/PT from 41.72/14.38/15.86 s (WPHV_C) to 62.08/23.38/22.22 s. In in vivo studies, WPHVC_V1 achieved tail thrombus inhibition in the mouse tail by reducing the length of the thrombus from 3.562 cm in CK to 1.853 cm (1.729 cm for sodium heparin and 2.530 cm for WPHV_C), completely inhibited thrombus formation in a carotid artery model and reduced tail bleeding time by 35.2 s compared with heparin sodium. Safety evaluations revealed that WPHVC_V1 did not cause hemolysis, had no significant effect on blood pressure or cause pathological changes in major organs.

CONCLUSION

These findings provide an initial foundation and sequence reference for the development of safe and effective anticoagulant drugs with potential for clinical translation.

Computational Design of a Bicyclic Peptide Inhibitor Targeting the ICOS/ICOS-L Protein-Protein Interaction

The interaction between the inducible T-cell costimulatory molecule (ICOS) and its ligand (ICOS-L) is a critical pathway in T-cell activation and immune regulation. We computationally designed a bicyclic peptide (CP5) that inhibits the ICOS/ICOS-L protein-protein interaction (PPI). Using the structural insights derived from the ICOS/ICOS-L co-crystal structure (PDB: 6X4G) and bias-exchange metadynamics simulations (BE-META), we first designed monocyclic peptide candidates containing the β-strand (residues 51-55 51YVYWQ55) of ICOS-L that interact with ICOS. Using Rosetta's flex ddG calculations and further disulfide-bond restraint, we arrived at CP5 (cyclo-RVY[CQPGWC]WVLpG) as a potential ICOS/ICOS-L inhibitor. Using dynamic light scattering (DLS), we examined the interaction between CP5 and ICOS. Importantly, we validated the ICOS/ICOS-L inhibitory activity of CP5 using both TR-FRET assay and ELISA. Notably, CP5 demonstrated satisfactory in vitro pharmacokinetic properties, such as metabolic stability and lipophilicity, positioning it as a promising candidate for further drug development. Our findings provide a foundation for future drug discovery efforts aiming to develop cyclic peptides that specifically target the ICOS/ICOS-L interaction.

Impaired chaperone-mediated autophagy leads to abnormal SORT1 (sortilin 1) turnover and CES1-dependent triglyceride hydrolysis

SORT1 (sortilin 1), a member of the the Vps10 (vacuolar protein sorting 10) family, is involved in hepatic lipid metabolism by regulating very low-density lipoprotein (VLDL) secretion and facilitating the lysosomal degradation of CES1 (carboxylesterase 1), crucial for triglyceride (TG) breakdown in the liver. This study explores whether SORT1 is targeted for degradation by chaperone-mediated autophagy (CMA), a selective protein degradation pathway that directs proteins containing KFERQ-like motifs to lysosomes via LAMP2A (lysosomal-associated membrane protein 2A). Silencing LAMP2A or HSPA8/Hsc70 with siRNA increased cytosolic SORT1 protein levels. Leupeptin treatment induced lysosomal accumulation of SORT1, unaffected by siLAMP2A co-treatment, indicating CMA-dependent degradation. Human SORT1 contains five KFERQ-like motifs (658VVTKQ662, 730VREVK734, 733VKDLK737, 734KDLKK738, and 735DLKKK739), crucial for HSPA8 recognition; mutating any single amino acid within these motifs decreased HSPA8 binding. Furthermore, compromised CMA activity resulted in elevated SORT1-mediated degradation of CES1, contributing to increased lipid accumulation in hepatocytes. Consistent with in vitro findings, LAMP2A knockdown in mice exacerbated high-fructose diet-induced fatty liver, marked by increased SORT1 and decreased CES1 levels. Conversely, LAMP2A overexpression promoted SORT1 degradation and CES1D accumulation, counteracting fasting-induced CES1D suppression through CMA activation. Our findings reveal that SORT1 is a substrate of CMA, highlighting its crucial role in directing CES1 to lysosomes. Consequently, disrupting CMA-mediated SORT1 degradation significantly affects CES1-dependent TG hydrolysis, thereby affecting hepatic lipid homeostasis.Abbreviations: APOB: apolipoprotein B; CES1: carboxylesterase 1; CMA: chaperone-mediated autophagy; HSPA8/Hsc70: heat shock protein family A (Hsp70) member 8; LAMP2A: lysosomal associated membrane protein 2A; LDL-C: low-density lipoprotein-cholesterol; PLIN: perilipin; SORT1: sortilin 1; TG: triglyceride; VLDL: very low-density lipoprotein; Vps10: vacuolar protein sorting 10.

Preemptive optimization of a clinical antibody for broad neutralization of SARS-CoV-2 variants and robustness against viral escape

Most previously authorized clinical antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have lost neutralizing activity to recent variants due to rapid viral evolution. To mitigate such escape, we preemptively enhance AZD3152, an antibody authorized for prophylaxis in immunocompromised individuals. Using deep mutational scanning (DMS) on the SARS-CoV-2 antigen, we identify AZD3152 vulnerabilities at antigen positions F456 and D420. Through two iterations of computational antibody design that integrates structure-based modeling, machine-learning, and experimental validation, we co-optimize AZD3152 against 24 contemporary and previous SARS-CoV-2 variants, as well as 20 potential future escape variants. Our top candidate, 3152-1142, restores full potency (100-fold improvement) against the more recently emerged XBB.1.5+F456L variant that escaped AZD3152, maintains potency against previous variants of concern, and shows no additional vulnerability as assessed by DMS. This preemptive mitigation demonstrates a generalizable approach for optimizing existing antibodies against potential future viral escape.

PCANN Program for Structure-Based Prediction of Protein-Protein Binding Affinity: Comparison With Other Neural-Network Predictors

In this communication, we introduce a new structure-based affinity predictor for protein-protein complexes. This predictor, dubbed PCANN (Protein Complex Affinity by Neural Network), uses the ESM-2 language model to encode the information about protein binding interfaces and graph attention network (GAT) to parlay this information intoK d $$ {K}_{\mathrm{d}} $$ predictions. In the tests employing two previously unused literature-extracted datasets, PCANN performed better than the best of the publicly available predictors, BindPPI, with mean absolute error (MAE) of 1.3 versus 1.4 kcal/mol. Further progress in the development ofK d $$ {K}_{\mathrm{d}} $$ predictors using deep learning models is faced with two problems: (i) the amount of experimental data available to train and test new predictors is limited and (ii) the availableK d $$ {K}_{\mathrm{d}} $$ data are often not very accurate and lack internal consistency with respect to measurement conditions. These issues can be potentially addressed through an AI-leveraged literature search followed by careful human curation and by introducing additional parameters to account for variations in experimental conditions.

Prospective Evaluation of Structure-Based Simulations Reveal Their Ability to Predict the Impact of Kinase Mutations on Inhibitor Binding

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as ΔΔG). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate ΔΔG of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.

Learning the language of protein-protein interactions

Protein Language Models (PLMs) trained on large databases of protein sequences have proven effective in modeling protein biology across a wide range of applications. However, while PLMs excel at capturing individual protein properties, they face challenges in natively representing protein-protein interactions (PPIs), which are crucial to understanding cellular processes and disease mechanisms. Here, we introduce MINT, a PLM specifically designed to model sets of interacting proteins in a contextual and scalable manner. Using unsupervised training on a large curated PPI dataset derived from the STRING database, MINT outperforms existing PLMs in diverse tasks relating to protein-protein interactions, including binding affinity prediction and estimation of mutational effects. Beyond these core capabilities, it excels at modeling interactions in complex protein assemblies and surpasses specialized models in antibody-antigen modeling and T cell receptor-epitope binding prediction. MINT's predictions of mutational impacts on oncogenic PPIs align with experimental studies, and it provides reliable estimates for the potential for cross-neutralization of antibodies against SARS-CoV-2 variants of concern. These findings position MINT as a powerful tool for elucidating complex protein interactions, with significant implications for biomedical research and therapeutic discovery.

Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as ΔΔG). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate ΔΔG of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.

Decoding the functional impact of the cancer genome through protein-protein interactions

Acquisition of genomic mutations enables cancer cells to gain fitness advantages under selective pressure and, ultimately, leads to oncogenic transformation. Interestingly, driver mutations, even within the same gene, can yield distinct phenotypes and clinical outcomes, necessitating a mutation-focused approach. Conversely, cellular functions are governed by molecular machines and signalling networks that are mostly controlled by protein-protein interactions (PPIs). The functional impact of individual genomic alterations could be transmitted through regulated nodes and hubs of PPIs. Oncogenic mutations may lead to modified residues of proteins, enabling interactions with other proteins that the wild-type protein does not typically interact with, or preventing interactions with proteins that the wild-type protein usually interacts with. This can result in the rewiring of molecular signalling cascades and the acquisition of an oncogenic phenotype. Here, we review the altered PPIs driven by oncogenic mutations, discuss technologies for monitoring PPIs and provide a functional analysis of mutation-directed PPIs. These driver mutation-enabled PPIs and mutation-perturbed PPIs present a new paradigm for the development of tumour-specific therapeutics. The intersection of cancer variants and altered PPI interfaces represents a new frontier for understanding oncogenic rewiring and developing tumour-selective therapeutic strategies.

Decoding the effects of mutation on protein interactions using machine learning

Accurately predicting mutation-caused binding free energy changes (ΔΔGs) on protein interactions is crucial for understanding how genetic variations affect interactions between proteins and other biomolecules, such as proteins, DNA/RNA, and ligands, which are vital for regulating numerous biological processes. Developing computational approaches with high accuracy and efficiency is critical for elucidating the mechanisms underlying various diseases, identifying potential biomarkers for early diagnosis, and developing targeted therapies. This review provides a comprehensive overview of recent advancements in predicting the impact of mutations on protein interactions across different interaction types, which are central to understanding biological processes and disease mechanisms, including cancer. We summarize recent progress in predictive approaches, including physicochemical-based, machine learning, and deep learning methods, evaluating the strengths and limitations of each. Additionally, we discuss the challenges related to the limitations of mutational data, including biases, data quality, and dataset size, and explore the difficulties in developing accurate prediction tools for mutation-induced effects on protein interactions. Finally, we discuss future directions for advancing these computational tools, highlighting the capabilities of advancing technologies, such as artificial intelligence to drive significant improvements in mutational effects prediction.

Unraveling the molecular basis of substrate specificity and halogen activation in vanadium-dependent haloperoxidases

Vanadium-dependent haloperoxidases (VHPOs) are biotechnologically valuable and operationally versatile biocatalysts. VHPOs share remarkable active-site structural similarities yet display variable reactivity and selectivity. The factors dictating substrate specificity and, thus, a general understanding of VHPO reaction control still need to be discovered. This work's strategic single-point mutation in the cyanobacterial bromoperoxidase AmVHPO facilitates a selectivity switch to allow aryl chlorination. This mutation induces loop formation that interacts with the neighboring protein monomer, creating a tunnel to the active sites. Structural analysis of the substrate-R425S-mutant complex reveals a substrate-binding site at the interface of two adjacent units. There, residues Glu139 and Phe401 interact with arenes, extending the substrate residence time close to the vanadate cofactor and stabilizing intermediates. Our findings validate the long-debated existence of direct substrate binding and provide a detailed VHPO mechanistic understanding. This work will pave the way for a broader application of VHPOs in diverse chemical processes.

In Silico Screening, Molecular Dynamics Simulation and Binding Free Energy Identify Single-Point Mutations That Destabilize p53 and Reduce Binding to DNA

Considering p53's pivotal role as a tumor suppressor protein, proactive identification and characterization of potentially harmful p53 mutations are crucial before they appear in the population. To address this, four computational prediction tools-SIFT, Polyphen-2, PhD-SNP, and MutPred2-utilizing sequence-based and machine-learning algorithms, were employed to identify potentially deleterious p53 nsSNPs (nonsynonymous single nucleotide polymorphisms) that may impact p53 structure, dynamics, and binding with DNA. These computational methods identified three variants, namely, C141Y, C238S, and L265P, as detrimental to p53 stability. Furthermore, molecular dynamics (MD) simulations revealed that all three variants exhibited heightened structural flexibility compared to the native protein, especially the C141Y and L265P mutations. Consequently, due to the altered structure of mutant p53, the DNA-binding affinity of all three variants decreased by approximately 1.8 to 9.7 times compared to wild-type p53 binding with DNA (14 μM). Notably, the L265P mutation exhibited an approximately ten-fold greater reduction in binding affinity. Consequently, the presence of the L265P mutation in p53 could pose a substantial risk to humans. Given that p53 regulates abnormal tumor growth, this research carries significant implications for surveillance efforts and the development of anticancer therapies.

Combining computational modeling and experimental library screening to affinity-mature VEEV-neutralizing antibody F5

Engineered monoclonal antibodies have proven to be highly effective therapeutics in recent viral outbreaks. However, despite technical advancements, an ability to rapidly adapt or increase antibody affinity and by extension, therapeutic efficacy, has yet to be fully realized. We endeavored to stand-up such a pipeline using molecular modeling combined with experimental library screening to increase the affinity of F5, a monoclonal antibody with potent neutralizing activity against Venezuelan Equine Encephalitis Virus (VEEV), to recombinant VEEV (IAB) E1E2 antigen. We modeled the F5/E1E2 binding interface and generated predictions for mutations to improve binding using a Rosetta-based approach and dTERMen, an informatics approach. The modeling was complicated by the fact that a high-resolution structure of F5 is not available and the H3 loop of F5 exceeds the length for which current modeling approaches can determine a unique structure. A subset of the predicted mutations from both methods were incorporated into a phage display library of scFvs. This library and a library generated by error-prone PCR were screened for binding affinity to the recombinant antigen. Results from the screens identified favorable mutations which were incorporated into 12 human-IgG1 variants. The best variant, containing eight mutations, improved KD from 0.63 nM (parental) to 0.01 nM. While this did not improve neutralization or therapeutic potency of F5 against IAB, it did increase cross-reactivity to other closely related VEEV epizootic and enzootic strains, demonstrating the potential of this method to rapidly adapt existing therapeutics to emerging viral strains.

Shaping current European mitochondrial haplogroup frequency in response to infection: the case of SARS-CoV-2 severity

The frequency of mitochondrial DNA haplogroups (mtDNA-HG) in humans is known to be shaped by migration and repopulation. Mounting evidence indicates that mtDNA-HG are not phenotypically neutral, and selection may contribute to its distribution. Haplogroup H, the most abundant in Europe, improved survival in sepsis. Here we developed a random forest trained model for mitochondrial haplogroup calling using data procured from GWAS arrays. Our results reveal that in the context of the SARS-CoV-2 pandemic, HV branch were found to represent protective factors against the development of critical SARS-CoV-2 in an analysis of 14,349 patients. These results highlight the role of mtDNA in the response to infectious diseases and support the proposal that its expansion and population proportion has been influenced by selection through successive pandemics.

The antitumor peptide M1-20 induced the degradation of CDK1 through CUL4-DDB1-DCAF1-involved ubiquitination

CDK1 is an oncogenic serine/threonine kinase known to play an important role in the regulation of the cell cycle. FOXM1, as one of the CDK1 substrates, requires binding of CDK1/CCNB1 complex for phosphorylation-dependent recruitment of p300/CBP coactivators to mediate transcriptional activity. Previous studies from our laboratory found that a novel peptide (M1-20) derived from the C-terminus of FOXM1 exhibited potent inhibitory effects for cancer cells. Based on these proofs and to explore the inhibitory mechanism of M1-20, we designed experiments and found that CDK1 served as an important target of M1-20. M1-20 enhanced the ubiquitination and degradation of CDK1 by CUL4-DDB1-DCAF1 complexes through the proteasome pathway. M1-20 could also affect the formation of CDK1/CCNB1 complexes. In addition, compared to RO3306, a CDK1 inhibitor, M1-20 exhibited excellent inhibitory effects in FVB/N MMTV-PyVT murine model of spontaneous breast cancer. These results suggested that M1-20 was a potential CDK1 inhibitor for the treatment of cancer.

Construction and enzymatic characterization of a monomeric variant of dimeric amylosucrase from Deinococcus geothermalis

Amylosucrase (ASase; E.C. 2.4.1.4), a member of glycoside hydrolase family 13 (GH13), produces α-1,4-glucans and sucrose isomers using sucrose as its sole substrate. This study identifies and characterizes the dimeric structure of ASase from Deinococcus geothermalis (DgAS), highlighting essential amino acid residues for maintaining the dimeric state. The monomeric form, DgAS R30A, exhibited a higher affinity for sucrose compared to the wild-type (WT), especially during the formation of the ASase-glucose intermediate complex and subsequent hydrolysis. Notably, DgAS R30A produced a higher proportion of α-glucans with a degree of polymerization (DP) of ≤20 and fewer α-glucans with a DP of ≥31. This suggested that the reduced surface area of the oligosaccharide binding site in the monomeric form led to decreased binding of longer-chain maltooligosaccharides, favoring the formation of shorter DP α-glucans. Kinetic analysis revealed significantly lower Michaelis constants (Km) for DgAS R30A's total and hydrolysis activities, with the overall performance (kcat/Km) values for DgAS R30A exceeded those of the WT at all sucrose concentrations. Here, we report the first high-resolution homodimeric DgAS structure, revealing conserved active site residues and a unique dimerization interface. This study enhances our understanding of the molecular factors influencing the oligomeric state and enzyme activities.

ESM-scan-A tool to guide amino acid substitutions

Protein structure prediction and (re)design have gone through a revolution in the last 3 years. The tremendous progress in these fields has been almost exclusively driven by readily available machine learning algorithms applied to protein folding and sequence design problems. Despite these advancements, predicting site-specific mutational effects on protein stability and function remains an unsolved problem. This is a persistent challenge, mainly because the free energy of large systems is very difficult to compute with absolute accuracy and subtle changes to protein structures are hard to capture with computational models. Here, we describe the implementation and use of ESM-Scan, which uses the ESM zero-shot predictor to scan entire protein sequences for preferential amino acid changes, thus enabling in silico deep mutational scanning experiments. We benchmark ESM-Scan on its predictive capabilities for stability and functionality of sequence changes using three publicly available datasets and proceed by experimentally testing the tool's performance on a challenging test case of a blue-light-activated diguanylate cyclase from Methylotenera species (MsLadC), where it accurately predicted the importance of a highly conserved residue in a region involved in allosteric product inhibition. Our experimental results show that the ESM-zero shot model is capable of inferring the effects of a set of amino acid substitutions in their correlation between predicted fitness and experimental results. ESM-Scan is publicly available at https://huggingface.co/spaces/thaidaev/zsp.

Categorization of hotspots into three types - weak, moderate and strong to distinguish protein-protein versus protein-peptide interactions

Protein-protein and protein-peptide interactions (PPI and PPepI) belong to a similar category of interactions, yet seemingly subtle differences exist among them. To characterize differences between protein-protein (PP) and protein-peptide (PPep) interactions, we have focussed on two important classes of residues-hotspot and anchor residues. Using implicit solvation-based free energy calculations, a very large-scale alanine scanning has been performed on benchmark datasets, consisting of over 5700 interface residues. The differences in the two categories are more pronounced, if the data were divided into three distinct types, namely - weak hotspots (having binding free energy loss upon Ala mutation, ΔΔG, ∼2-10 kcal/mol), moderate hotspots (ΔΔG, ∼10-20 kcal/mol) and strong hotspots (ΔΔG ≥ ∼20 kcal/mol). The analysis suggests that for PPI, weak hotspots are predominantly populated by polar and hydrophobic residues. The distribution shifts towards charged and polar residues for moderate hotspot and charged residues (principally Arg) are overwhelmingly present in the strong hotspot. On the other hand, in the PPepI dataset, the distribution shifts from predominantly hydrophobic and polar (in the weak type) to almost similar preference for polar, hydrophobic and charged residues (in moderate type) and finally the charged residue (Arg) and Trp are mostly occupied in the strong type. The preferred anchor residues in both categories are Arg, Tyr and Leu, possessing bulky side chain and which also strike a delicate balance between side chain flexibility and rigidity. The present knowledge should aid in effective design of biologics, by augmentation or disruption of PPIs with peptides or peptidomimetics.Communicated by Ramaswamy H. Sarma.

Distinct pathways for evolution of enhanced receptor binding and cell entry in SARS-like bat coronaviruses

Understanding the zoonotic risks posed by bat coronaviruses (CoVs) is critical for pandemic preparedness. Herein, we generated recombinant vesicular stomatitis viruses (rVSVs) bearing spikes from divergent bat CoVs to investigate their cell entry mechanisms. Unexpectedly, the successful recovery of rVSVs bearing the spike from SHC014-CoV, a SARS-like bat CoV, was associated with the acquisition of a novel substitution in the S2 fusion peptide-proximal region (FPPR). This substitution enhanced viral entry in both VSV and coronavirus contexts by increasing the availability of the spike receptor-binding domain to recognize its cellular receptor, ACE2. A second substitution in the S1 N-terminal domain, uncovered through the rescue and serial passage of a virus bearing the FPPR substitution, further enhanced spike:ACE2 interaction and viral entry. Our findings identify genetic pathways for adaptation by bat CoVs during spillover and host-to-host transmission, fitness trade-offs inherent to these pathways, and potential Achilles' heels that could be targeted with countermeasures.

Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions

Assessing mutation impact on the binding affinity change (ΔΔG) of protein-protein interactions (PPIs) plays a crucial role in unraveling structural-functional intricacies of proteins and developing innovative protein designs. In this study, we present a deep learning framework, PIANO, for improved prediction of ΔΔG in PPIs. The PIANO framework leverages a graph masked self-distillation scheme for protein structural geometric representation pre-training, which effectively captures the structural context representations surrounding mutation sites, and makes predictions using a multi-branch network consisting of multiple encoders for amino acids, atoms, and protein sequences. Extensive experiments demonstrated its superior prediction performance and the capability of pre-trained encoder in capturing meaningful representations. Compared to previous methods, PIANO can be widely applied on both holo complex structures and apo monomer structures. Moreover, we illustrated the practical applicability of PIANO in highlighting pathogenic mutations and crucial proteins, and distinguishing de novo mutations in disease cases and controls in PPI systems. Overall, PIANO offers a powerful deep learning tool, which may provide valuable insights into the study of drug design, therapeutic intervention, and protein engineering.

Analysis of EGFR binding hotspots for design of new EGFR inhibitory biologics

The epidermal growth factor (EGF) receptor (EGFR) is activated by the binding of one of seven EGF-like ligands to its ectodomain. Ligand binding results in EGFR dimerization and stabilization of the active receptor conformation subsequently leading to activation of downstream signaling. Aberrant activation of EGFR contributes to cancer progression through EGFR overexpression/amplification, modulation of its positive and negative regulators, and/or activating mutations within EGFR. EGFR targeted therapeutic antibodies prevent dimerization and interaction with endogenous ligands by binding the ectodomain of EGFR. However, these antibodies have had limited success in the clinic, partially due to EGFR ectodomain resistance mutations, and are only applicable to a subset of patients with EGFR-driven cancers. These limitations suggest that alternative EGFR targeted biologics need to be explored for EGFR-driven cancer therapy. To this end, we analyze the EGFR interfaces of known inhibitory biologics with determined structures in the context of endogenous ligands, using the Rosetta macromolecular modeling software to highlight the most important interactions on a per-residue basis. We use this analysis to identify the structural determinants of EGFR targeted biologics. We suggest that commonly observed binding motifs serve as the basis for rational design of new EGFR targeted biologics, such as peptides, antibodies, and nanobodies.

PPI-hotspotID for detecting protein-protein interaction hot spots from the free protein structure

Experimental detection of residues critical for protein-protein interactions (PPI) is a time-consuming, costly, and labor-intensive process. Hence, high-throughput PPI-hot spot prediction methods have been developed, but they have been validated using relatively small datasets, which may compromise their predictive reliability. Here, we introduce PPI-hotspotID, a novel method for identifying PPI-hot spots using the free protein structure, and validated it on the largest collection of experimentally confirmed PPI-hot spots to date. We explored the possibility of detecting PPI-hot spots using (i) FTMap in the PPI mode, which identifies hot spots on protein-protein interfaces from the free protein structure, and (ii) the interface residues predicted by AlphaFold-Multimer. PPI-hotspotID yielded better performance than FTMap and SPOTONE, a webserver for predicting PPI-hot spots given the protein sequence. When combined with the AlphaFold-Multimer-predicted interface residues, PPI-hotspotID yielded better performance than either method alone. Furthermore, we experimentally verified several PPI-hotspotID-predicted PPI-hot spots of eukaryotic elongation factor 2. Notably, PPI-hotspotID can reveal PPI-hot spots not obvious from complex structures, including those in indirect contact with binding partners. PPI-hotspotID serves as a valuable tool for understanding PPI mechanisms and aiding drug design. It is available as a web server (https://ppihotspotid.limlab.dnsalias.org/) and open-source code (https://github.com/wrigjz/ppihotspotid/).

Pretrainable geometric graph neural network for antibody affinity maturation

Increasing the binding affinity of an antibody to its target antigen is a crucial task in antibody therapeutics development. This paper presents a pretrainable geometric graph neural network, GearBind, and explores its potential in in silico affinity maturation. Leveraging multi-relational graph construction, multi-level geometric message passing and contrastive pretraining on mass-scale, unlabeled protein structural data, GearBind outperforms previous state-of-the-art approaches on SKEMPI and an independent test set. A powerful ensemble model based on GearBind is then derived and used to successfully enhance the binding of two antibodies with distinct formats and target antigens. ELISA EC50 values of the designed antibody mutants are decreased by up to 17 fold, and KD values by up to 6.1 fold. These promising results underscore the utility of geometric deep learning and effective pretraining in macromolecule interaction modeling tasks.

Robust Prediction of Relative Binding Energies for Protein-Protein Complex Mutations Using Free Energy Perturbation Calculations

Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how Protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.

Exploring a Hirudin variant from nonhematophagous leeches: Unraveling full-length sequence, alternative splicing, function, and potential as a novel anticoagulant polypeptide

ETHNOPHARMACOLOGICAL RELEVANCE

Leeches exhibit robust anticoagulant activity, making them useful for treating cardiovascular diseases in traditional Chinese medicine. Whitmania pigra, the primary source species of leech-derived medicinal compounds in China, has been demonstrated to possess formidable anticoagulant properties. Hirudin-like peptides, recognized as potent thrombin inhibitors, are prevalent in hematophagous leeches. Considering that W. pigra is a nonhematophagic leech, the following question arises: does a hirudin variant exist in this species?

AIM OF THE STUDY

In this study we identified the hirudin-encoding gene (WP_HV1) in the W. pigra genome. The goal of this study was to assess its anticoagulant activity and analyze the related mechanisms.

MATERIALS AND METHODS

In this study, a hirudin-encoding gene, WP_HV1, was identified from the W. pigra genome, and its accurate coding sequence (CDS) was validated through cloning from cDNA extracted from fresh W. pigra specimens. The structure of WP_HV1 and the amino acids associated with its anticoagulant activity were determined by sequence and structural analysis and prediction of its binding energy to thrombin. E. coli was used for the expression of WP_HV1 and recombinant proteins with various structures and mutants. The anticoagulant activity of the synthesized recombinant proteins was then confirmed using thrombin time (TT).

RESULTS

Validation of the WP_HV1 gene was accomplished, and three alternative splices were discovered. The TT of the blank sample exceeded that of the recombinant WP_HV1 sample by 1.74 times (0.05 mg/ml), indicating positive anticoagulant activity. The anticoagulant activity of WP_HV1 was found to be associated with its C-terminal tyrosine, along with the presence of 9 acidic amino acids on both the left and right sides. A significant reduction in the corresponding TT was observed for the mutated amino acids compared to those of the wild type, with decreases of 4.8, 6.6, and 3.9 s, respectively. In addition, the anticoagulant activity of WP_HV1 was enhanced and prolonged for 2.7 s when the lysine-67 residue was mutated to tryptophan.

CONCLUSION

Only one hirudin-encoding variant was identified in W. pigra. The active amino acids associated with anticoagulation in WP_HV1 were resolved and validated, revealing a novel source for screening and developing new anticoagulant drugs.

Divergent folding-mediated epistasis among unstable membrane protein variants

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.

AI-based IsAb2.0 for antibody design

Therapeutic antibody design has garnered widespread attention, highlighting its interdisciplinary importance. Advancements in technology emphasize the critical role of designing nanobodies and humanized antibodies in antibody engineering. However, current experimental methods are costly and time-consuming. Computational approaches, while progressing, faced limitations due to insufficient structural data and the absence of a standardized protocol. To tackle these challenges, our lab previously developed IsAb1.0, an in silico antibody design protocol. Yet, IsAb1.0 lacked accuracy, had a complex procedure, and required extensive antibody bioinformation. Moreover, it overlooked nanobody and humanized antibody design, hindering therapeutic antibody development. Building upon IsAb1.0, we enhanced our design protocol with artificial intelligence methods to create IsAb2.0. IsAb2.0 utilized AlphaFold-Multimer (2.3/3.0) for accurate modeling and complex construction without templates and employed the precise FlexddG method for in silico antibody optimization. Validated through optimization of a humanized nanobody J3 (HuJ3) targeting HIV-1 gp120, IsAb2.0 predicted five mutations that can improve HuJ3-gp120 binding affinity. These predictions were confirmed by commercial software and validated through binding and neutralization assays. IsAb2.0 streamlined antibody design, offering insights into future techniques to accelerate immunotherapy development.

Distinct pathway for evolution of enhanced receptor binding and cell entry in SARS-like bat coronaviruses

Understanding the zoonotic risks posed by bat coronaviruses (CoVs) is critical for pandemic preparedness. Herein, we generated recombinant vesicular stomatitis viruses (rVSVs) bearing spikes from divergent bat CoVs to investigate their cell entry mechanisms. Unexpectedly, the successful recovery of rVSVs bearing the spike from SHC014, a SARS-like bat CoV, was associated with the acquisition of a novel substitution in the S2 fusion peptide-proximal region (FPPR). This substitution enhanced viral entry in both VSV and coronavirus contexts by increasing the availability of the spike receptor-binding domain to recognize its cellular receptor, ACE2. A second substitution in the spike N-terminal domain, uncovered through forward-genetic selection, interacted epistatically with the FPPR substitution to synergistically enhance spike:ACE2 interaction and viral entry. Our findings identify genetic pathways for adaptation by bat CoVs during spillover and host-to-host transmission, fitness trade-offs inherent to these pathways, and potential Achilles' heels that could be targeted with countermeasures.

DDAffinity: predicting the changes in binding affinity of multiple point mutations using protein 3D structure

MOTIVATION

Mutations are the crucial driving force for biological evolution as they can disrupt protein stability and protein-protein interactions which have notable impacts on protein structure, function, and expression. However, existing computational methods for protein mutation effects prediction are generally limited to single point mutations with global dependencies, and do not systematically take into account the local and global synergistic epistasis inherent in multiple point mutations.

RESULTS

To this end, we propose a novel spatial and sequential message passing neural network, named DDAffinity, to predict the changes in binding affinity caused by multiple point mutations based on protein 3D structures. Specifically, instead of being on the whole protein, we perform message passing on the k-nearest neighbor residue graphs to extract pocket features of the protein 3D structures. Furthermore, to learn global topological features, a two-step additive Gaussian noising strategy during training is applied to blur out local details of protein geometry. We evaluate DDAffinity on benchmark datasets and external validation datasets. Overall, the predictive performance of DDAffinity is significantly improved compared with state-of-the-art baselines on multiple point mutations, including end-to-end and pre-training based methods. The ablation studies indicate the reasonable design of all components of DDAffinity. In addition, applications in nonredundant blind testing, predicting mutation effects of SARS-CoV-2 RBD variants, and optimizing human antibody against SARS-CoV-2 illustrate the effectiveness of DDAffinity.

AVAILABILITY AND IMPLEMENTATION

DDAffinity is available at https://github.com/ak422/DDAffinity.

Computational and experimental identification of keystone interactions in Ebola virus matrix protein VP40 dimer formation

The Ebola virus (EBOV) is a lipid-enveloped virus with a negative sense RNA genome that can cause severe and often fatal viral hemorrhagic fever. The assembly and budding of EBOV is regulated by the matrix protein, VP40, which is a peripheral protein that associates with anionic lipids at the inner leaflet of the plasma membrane. VP40 is sufficient to form virus-like particles (VLPs) from cells, which are nearly indistinguishable from authentic virions. Due to the restrictions of studying EBOV in BSL-4 facilities, VP40 has served as a surrogate in cellular studies to examine the EBOV assembly and budding process from the host cell plasma membrane. VP40 is a dimer where inhibition of dimer formation halts budding and formation of new VLPs as well as VP40 localization to the plasma membrane inner leaflet. To better understand VP40 dimer stability and critical amino acids to VP40 dimer formation, we integrated computational approaches with experimental validation. Site saturation/alanine scanning calculation, combined with molecular mechanics-based generalized Born with Poisson-Boltzmann surface area (MM-GB/PBSA) method and molecular dynamics simulations were used to predict the energetic contribution of amino acids to VP40 dimer stability and the hydrogen bonding network across the dimer interface. These studies revealed several previously unknown interactions and critical residues predicted to impact VP40 dimer formation. In vitro and cellular studies were then pursued for a subset of VP40 mutations demonstrating reduction in dimer formation (in vitro) or plasma membrane localization (in cells). Together, the computational and experimental approaches revealed critical residues for VP40 dimer stability in an alpha-helical interface (between residues 106-117) as well as in a loop region (between residues 52-61) below this alpha-helical region. This study sheds light on the structural origins of VP40 dimer formation and may inform the design of a small molecule that can disrupt VP40 dimer stability.

Computationally restoring the potency of a clinical antibody against Omicron

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs1-3 and revealed how quickly viral escape can curtail effective options4,5. When the SARS-CoV-2 Omicron variant emerged in 2021, many antibody drug products lost potency, including Evusheld and its constituent, cilgavimab4-6. Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination4 and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign and renew the efficacy of COV2-2130 against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and subsequent variants of concern, and provides protection in vivo against the strains tested: WA1/2020, BA.1.1 and BA.5. Deep mutational scanning of tens of thousands of pseudovirus variants reveals that 2130-1-0114-112 improves broad potency without increasing escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Our computational approach does not require experimental iterations or pre-existing binding data, thus enabling rapid response strategies to address escape variants or lessen escape vulnerabilities.

Use of phosphotyrosine-containing peptides to target SH2 domains: Antagonist peptides of the Crk/CrkL-p130Cas axis

Protein-protein interactions between SH2 domains and segments of proteins that include a post-translationally phosphorylated tyrosine residue (pY) underpin numerous signal transduction cascades that allow cells to respond to their environment. Dysregulation of the writing, erasing, and reading of these posttranslational modifications is a hallmark of human disease, notably cancer. Elucidating the precise role of the SH2 domain-containing adaptor proteins Crk and CrkL in tumor cell migration and invasion is challenging because there are no specific and potent antagonists available. Crk and CrkL SH2s interact with a region of the docking protein p130Cas containing 15 potential pY-containing tetrapeptide motifs. This chapter summarizes recent efforts toward peptide antagonists for this Crk/CrkL-p130Cas interaction. We describe our protocol for recombinant expression and purification of Crk and CrkL SH2s for functional assays and our procedure to determine the consensus binding motif from the p130Cas sequence. To develop a more potent antagonist, we employ methods often associated with structure-based drug design. Computational docking using Rosetta FlexPepDock, which accounts for peptides having a greater number of conformational degrees of freedom than small organic molecules that typically constitute libraries, provides quantitative docking metrics to prioritize candidate peptides for experimental testing. A battery of biophysical assays, including fluorescence polarization, differential scanning fluorimetry and saturation transfer difference nuclear magnetic resonance spectroscopy, were employed to assess the candidates. In parallel, GST pulldown competition assays characterized protein-protein binding in vitro. Taken together, our methodology yields peptide antagonists of the Crk/CrkL-p130Cas axis that will be used to validate targets, assess druggability, foster in vitro assay development, and potentially serve as lead compounds for therapeutic intervention.

Robust prediction of relative binding energies for protein-protein complex mutations using free energy perturbation calculations

Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.

Deep mutational scanning of Pneumocystis jirovecii dihydrofolate reductase reveals allosteric mechanism of resistance to an antifolate

Pneumocystis jirovecii is a fungal pathogen that causes pneumocystis pneumonia, a disease that mainly affects immunocompromised individuals. This fungus has historically been hard to study because of our inability to grow it in vitro. One of the main drug targets in P. jirovecii is its dihydrofolate reductase (PjDHFR). Here, by using functional complementation of the baker's yeast ortholog, we show that PjDHFR can be inhibited by the antifolate methotrexate in a dose-dependent manner. Using deep mutational scanning of PjDHFR, we identify mutations conferring resistance to methotrexate. Thirty-one sites spanning the protein have at least one mutation that leads to resistance, for a total of 355 high-confidence resistance mutations. Most resistance-inducing mutations are found inside the active site, and many are structurally equivalent to mutations known to lead to resistance to different antifolates in other organisms. Some sites show specific resistance mutations, where only a single substitution confers resistance, whereas others are more permissive, as several substitutions at these sites confer resistance. Surprisingly, one of the permissive sites (F199) is without direct contact to either ligand or cofactor, suggesting that it acts through an allosteric mechanism. Modeling changes in binding energy between F199 mutants and drug shows that most mutations destabilize interactions between the protein and the drug. This evidence points towards a more important role of this position in resistance than previously estimated and highlights potential unknown allosteric mechanisms of resistance to antifolate in DHFRs. Our results offer unprecedented resources for the interpretation of mutation effects in the main drug target of an uncultivable fungal pathogen.

New insights into GATOR2-dependent interactions and its conformational changes in amino acid sensing

Eukaryotic cells coordinate growth under different environmental conditions via mechanistic target of rapamycin complex 1 (mTORC1). In the amino-acid-sensing signalling pathway, the GATOR2 complex, containing five evolutionarily conserved subunits (WDR59, Mios, WDR24, Seh1L and Sec13), is required to regulate mTORC1 activity by interacting with upstream CASTOR1 (arginine sensor) and Sestrin2 (leucine sensor and downstream GATOR1 complex). GATOR2 complex utilizes β-propellers to engage with CASTOR1, Sestrin2 and GATOR1, removal of these β-propellers results in substantial loss of mTORC1 capacity. However, structural information regarding the interface between amino acid sensors and GATOR2 remains elusive. With the recent progress of the AI-based tool AlphaFold2 (AF2) for protein structure prediction, structural models were predicted for Sentrin2-WDR24-Seh1L and CASTOR1-Mios β-propeller. Furthermore, the effectiveness of relevant residues within the interface was examined using biochemical experiments combined with molecular dynamics (MD) simulations. Notably, fluorescence resonance energy transfer (FRET) analysis detected the structural transition of GATOR2 in response to amino acid signals, and the deletion of Mios β-propeller severely impeded that change at distinct arginine levels. These findings provide structural perspectives on the association between GATOR2 and amino acid sensors and can facilitate future research on structure determination and function.

Computational Mutagenesis of Antibody Fragments: Disentangling Side Chains from ΔΔG Predictions

The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.

Enhancing bezlotoxumab binding to C. difficile toxin B2: insights from computational simulations and mutational analyses for antibody design

Clostridioides difficile infection (CDI) is a significant concern caused by widespread antibiotic use, resulting in diarrhea and inflammation from the gram-positive anaerobic bacterium C. difficile. Although bezlotoxumab (Bez), a monoclonal antibody (mAb), was developed to address CDI recurrences, the recurrence rate remains high, partly due to reduced neutralization efficiency against toxin B2. In this study, we aimed to enhance the binding of Bez to C. difficile toxin B2 by combining computational simulations and mutational analyses. We identified specific mutations in Bez, including S28R, S31W/K, Y32R, S56W and G103D/S in the heavy chain (Hc), and S32F/H/R/W/Y in the light chain (Lc), which significantly improved binding to toxin B2 and formed critical protein-protein interactions. Through molecular dynamics simulations, several single mutations, such as HcS28R, LcS32H, LcS32R, LcS32W and LcS32Y, exhibited superior binding affinities to toxin B2 compared to Bez wild-type (WT), primarily attributed to Coulombic interactions. Combining the HcS28R mutation with four different mutations at residue LcS32 led to even greater binding affinities in double mutants (MTs), particularly HcS28R/LcS32H, HcS28R/LcS32R and HcS28R/LcS32Y, reinforcing protein-protein binding. Analysis of per-residue decomposition free energy highlighted key residues contributing significantly to enhanced binding interactions, emphasizing the role of electrostatic interactions. These findings offer insights into rational Bez MT design for improved toxin B2 binding, providing a foundation for developing more effective antibodies to neutralize toxin B2 and combat-related infections.

Targeting high-risk multiple myeloma genotypes with optimized anti-CD70 CAR-T cells

Despite the success of BCMA-targeting CAR-Ts in multiple myeloma, patients with high-risk cytogenetic features still relapse most quickly and are in urgent need of additional therapeutic options. Here, we identify CD70, widely recognized as a favorable immunotherapy target in other cancers, as a specifically upregulated cell surface antigen in high risk myeloma tumors. We use a structure-guided design to define a CD27-based anti-CD70 CAR-T design that outperforms all tested scFv-based CARs, leading to >80-fold improved CAR-T expansion in vivo. Epigenetic analysis via machine learning predicts key transcription factors and transcriptional networks driving CD70 upregulation in high risk myeloma. Dual-targeting CAR-Ts against either CD70 or BCMA demonstrate a potential strategy to avoid antigen escape-mediated resistance. Together, these findings support the promise of targeting CD70 with optimized CAR-Ts in myeloma as well as future clinical translation of this approach.

Engineering the thermal stability of a polyphosphate kinase by ancestral sequence reconstruction to expand the temperature boundary for an industrially applicable ATP regeneration system

ATP-dependent energy-consuming enzymatic reactions are widely used in cell-free biocatalysis. However, the direct addition of large amounts of expensive ATP can greatly increase cost, and enzymatic production is often difficult to achieve as a result. Although a polyphosphate kinase (PPK)-polyphosphate-based ATP regeneration system has the potential to solve this challenge, the generally poor thermal stability of PPKs limits the widespread use of this method. In this paper, we evaluated the thermal stability of a PPK from Sulfurovum lithotrophicum (SlPPK2). After directed evolution and computation-supported design, we found that SlPPK2 is very recalcitrant and cannot acquire beneficial mutations. Inspired by the usually outstanding stability of ancestral enzymes, we reconstructed the ancestral sequence of the PPK family and used it as a guide to construct three heat-stable variants of SlPPK2, of which the L35F/T144S variant has a half-life of more than 14 h at 60°C. Molecular dynamics simulations were performed on all enzymes to analyze the reasons for the increased thermal stability. The results showed that mutations at these two positions act synergistically from the interior and surface of the protein, leading to a more compact structure. Finally, the robustness of the L35F/T144S variant was verified in the synthesis of nucleotides at high temperature. In practice, the use of this high-temperature ATP regeneration system can effectively avoid byproduct accumulation. Our work extends the temperature boundary of ATP regeneration and has great potential for industrial applications.IMPORTANCEATP regeneration is an important basic applied study in the field of cell-free biocatalysis. Polyphosphate kinase (PPK) is an enzyme tool widely used for energy regeneration during enzymatic reactions. However, the thermal stability of the PPKs reported to date that can efficiently regenerate ATP is usually poor, which greatly limits their application. In this study, the thermal stability of a difficult-to-engineer PPK from Sulfurovum lithotrophicum was improved, guided by an ancestral sequence reconstruction strategy. The optimal variant has a 4.5-fold longer half-life at 60°C than the wild-type enzyme, thus enabling the extension of the temperature boundary for ATP regeneration. The ability of this variant to regenerate ATP was well demonstrated during high-temperature enzymatic production of nucleotides.

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

Divergent Folding-Mediated Epistasis Among Unstable Membrane Protein Variants

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.

Differential laboratory passaging of SARS-CoV-2 viral stocks impacts the in vitro assessment of neutralizing antibodies

Viral populations in natural infections can have a high degree of sequence diversity, which can directly impact immune escape. However, antibody potency is often tested in vitro with a relatively clonal viral populations, such as laboratory virus or pseudotyped virus stocks, which may not accurately represent the genetic diversity of circulating viral genotypes. This can affect the validity of viral phenotype assays, such as antibody neutralization assays. To address this issue, we tested whether recombinant virus carrying SARS-CoV-2 spike (VSV-SARS-CoV-2-S) stocks could be made more genetically diverse by passage, and if a stock passaged under selective pressure was more capable of escaping monoclonal antibody (mAb) neutralization than unpassaged stock or than viral stock passaged without selective pressures. We passaged VSV-SARS-CoV-2-S four times concurrently in three cell lines and then six times with or without polyclonal antiserum selection pressure. All three of the monoclonal antibodies tested neutralized the viral population present in the unpassaged stock. The viral inoculum derived from serial passage without antiserum selection pressure was neutralized by two of the three mAbs. However, the viral inoculum derived from serial passage under antiserum selection pressure escaped neutralization by all three mAbs. Deep sequencing revealed the rapid acquisition of multiple mutations associated with antibody escape in the VSV-SARS-CoV-2-S that had been passaged in the presence of antiserum, including key mutations present in currently circulating Omicron subvariants. These data indicate that viral stock that was generated under polyclonal antiserum selection pressure better reflects the natural environment of the circulating virus and may yield more biologically relevant outcomes in phenotypic assays. Thus, mAb assessment assays that utilize a more genetically diverse, biologically relevant, virus stock may yield data that are relevant for prediction of mAb efficacy and for enhancing biosurveillance.

Selection pressures on evolution of ribonuclease H explored with rigorous free-energy-based design

Understanding natural protein evolution and designing novel proteins are motivating interest in development of high-throughput methods to explore large sequence spaces. In this work, we demonstrate the application of multisite λ dynamics (MSλD), a rigorous free energy simulation method, and chemical denaturation experiments to quantify evolutionary selection pressure from sequence-stability relationships and to address questions of design. This study examines a mesophilic phylogenetic clade of ribonuclease H (RNase H), furthering its extensive characterization in earlier studies, focusing on E. coli RNase H (ecRNH) and a more stable consensus sequence (AncCcons) differing at 15 positions. The stabilities of 32,768 chimeras between these two sequences were computed using the MSλD framework. The most stable and least stable chimeras were predicted and tested along with several other sequences, revealing a designed chimera with approximately the same stability increase as AncCcons, but requiring only half the mutations. Comparing the computed stabilities with experiment for 12 sequences reveals a Pearson correlation of 0.86 and root mean squared error of 1.18 kcal/mol, an unprecedented level of accuracy well beyond less rigorous computational design methods. We then quantified selection pressure using a simple evolutionary model in which sequences are selected according to the Boltzmann factor of their stability. Selection temperatures from 110 to 168 K are estimated in three ways by comparing experimental and computational results to evolutionary models. These estimates indicate selection pressure is high, which has implications for evolutionary dynamics and for the accuracy required for design, and suggests accurate high-throughput computational methods like MSλD may enable more effective protein design.

Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants

Protein-protein binding affinity prediction is important for understanding complex biochemical pathways and to uncover protein interaction networks. Quantitative estimation of the binding affinity changes caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. The binding free energies of protein-protein complexes can be predicted using several computational tools. This chapter is a summary of software developed for the prediction of binding free energies for protein-protein complexes and their mutants.

A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations

Pathogen evolution of drug resistance often occurs in a stepwise manner via the accumulation of multiple mutations that in combination have a non-additive impact on fitness, a phenomenon known as epistasis. The evolution of resistance via the accumulation of point mutations in the DHFR genes of Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) has been studied extensively and multiple studies have shown epistatic interactions between these mutations determine the accessible evolutionary trajectories to highly resistant multiple mutations. Here, we simulated these evolutionary trajectories using a model of molecular evolution, parameterised using Rosetta Flex ddG predictions, where selection acts to reduce the target-drug binding affinity. We observe strong agreement with pathways determined using experimentally measured IC50 values of pyrimethamine binding, which suggests binding affinity is strongly predictive of resistance and epistasis in binding affinity strongly influences the order of fixation of resistance mutations. We also infer pathways directly from the frequency of mutations found in isolate data, and observe remarkable agreement with the most likely pathways predicted by our mechanistic model, as well as those determined experimentally. This suggests mutation frequency data can be used to intuitively infer evolutionary pathways, provided sufficient sampling of the population.

Development of IFNβ-1a versions with reduced immunogenicity and full in vitro biological activity for the treatment of multiple sclerosis

IFNβ (recombinant interferon Beta) has been widely used for the treatment of Multiple sclerosis for the last four decades. Despite the human origin of the IFNβ sequence, IFNβ is immunogenic, and unwanted immune responses in IFNβ-treated patients may compromise its efficacy and safety in the clinic. In this study, we applied the DeFT (De-immunization of Functional Therapeutics) approach to producing functional, de-immunized versions of IFNβ-1a. Two de-immunized versions of IFNβ-1a were produced in CHO cells and designated as IFNβ-1a(VAR1) and IFNβ-1a(VAR2). First, the secondary and tertiary protein structures were analyzed by circular dichroism spectroscopy. Then, the variants were also tested for functionality. While IFNβ-1a(VAR2) showed similar in vitro antiviral activity to the original protein, IFNβ-1a(VAR1) exhibited 40% more biological potency. Finally, in vivo assays using HLA-DR transgenic mice revealed that the de-immunized variants showed a markedly reduced immunogenicity when compared to the originator.

Alchemical Calculation of Relative Free Energies for Charge-Changing Mutations at Protein-Protein Interfaces Considering Fixed and Variable Protonation States

The calculation of relative free energies (ΔΔG) for charge-changing mutations at protein-protein interfaces through alchemical methods remains challenging due to variations in the system's net charge during charging steps, the possibility of mutated and contacting ionizable residues occurring in various protonation states, and undersampling issues. In this study, we present a set of strategies, collectively termed TIRST/TIRST-H+, to address some of these challenges. Our approaches combine thermodynamic integration (TI) with the prediction of pKa shifts to calculate ΔΔG values. Moreover, special sets of restraints are employed to keep the alchemically transformed molecules separated. The accuracy of the devised approaches was assessed on a large and diverse data set comprising 164 point mutations of charged residues (Asp, Glu, Lys, and Arg) to Ala at the protein-protein interfaces of complexes with known three-dimensional structures. Mean absolute and root-mean-square errors ranging from 1.38 to 1.66 and 1.89 to 2.44 kcal/mol, respectively, and Pearson correlation coefficients of ∼0.6 were obtained when testing the approaches on the selected data set using the GPU-TI module of Amber18 suite and the ff14SB force field. Furthermore, the inclusion of variable protonation states for the mutated acid residues improved the accuracy of the predicted ΔΔG values. Therefore, our results validate the use of TIRST/TIRST-H+ in prospective studies aimed at evaluating the impact of charge-changing mutations to Ala on the stability of protein-protein complexes.