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Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as "developability", which reflects an antibody's ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We find lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. We show that sequence DPs are more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies localize within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.
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Anticorpos Monoclonais , Engenharia de Proteínas , Humanos , Engenharia de Proteínas/métodos , Anticorpos Monoclonais/química , Anticorpos Monoclonais/imunologia , Conformação Proteica , Anticorpos de Cadeia Única/química , Anticorpos de Cadeia Única/genética , Anticorpos de Cadeia Única/imunologiaRESUMO
Summary: High-throughput sequencing (HTS) offers a modern, fast, and explorative solution to unveil the full potential of display techniques, like antibody phage display, in molecular biology. However, a significant challenge lies in the processing and analysis of such data. Furthermore, there is a notable absence of open-access user-friendly software tools that can be utilized by scientists lacking programming expertise. Here, we present ExpoSeq as an easy-to-use tool to explore, process, and visualize HTS data from antibody discovery campaigns like an expert while only requiring a beginner's knowledge. Availability and implementation: The pipeline is distributed via GitHub and PyPI, and it can either be installed as a package with pip or the user can choose to clone the repository.
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COVID-19 has resulted in millions of deaths and severe impact on economies worldwide. Moreover, the emergence of SARS-CoV-2 variants presented significant challenges in controlling the pandemic, particularly their potential to avoid the immune system and evade vaccine immunity. This has led to a growing need for research to predict how mutations in SARS-CoV-2 reduces the ability of antibodies to neutralize the virus. In this study, we assembled a set of 1813 mutations from the interface of SARS-CoV-2 spike protein's receptor binding domain (RBD) and neutralizing antibody complexes and developed a machine learning model to classify high or low escape mutations using interaction energy, inter-residue contacts and predicted binding free energy change. Our approach achieved an Area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.91 using the Random Forest classifier on the test dataset with 217 mutations. The model was further utilized to predict the escape mutations on a dataset of 29,165 mutations located at the interface of 83 RBD-neutralizing antibody complexes. A small subset of this dataset was also validated based on available experimental data. We found that top 10 % high escape mutations were dominated by charged to nonpolar mutations whereas low escape mutations were dominated by polar to nonpolar mutations. We believe that the present method will allow prioritization of high/low escape mutations in the context of neutralizing antibodies targeting SARS-CoV-2 RBD region and assist antibody design for current and emerging variants.
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COVID-19 , SARS-CoV-2 , Humanos , SARS-CoV-2/genética , COVID-19/genética , Anticorpos Antivirais/genética , Anticorpos Neutralizantes/genética , MutaçãoRESUMO
Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.
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Anticorpos , Regiões Determinantes de Complementaridade , Teorema de Bayes , Anticorpos/uso terapêutico , Regiões Determinantes de Complementaridade/genética , Cadeias Pesadas de Imunoglobulinas/química , AntígenosRESUMO
The expression of human epidermal growth factor receptor 2 (HER2) is a key classification factor in breast cancer. Many breast cancers express isoforms of HER2 with truncated carboxy-terminal fragments (CTF), collectively known as p95HER2. A common p95HER2 isoform, 611-CTF, is a biomarker for aggressive disease and confers resistance to therapy. Contrary to full-length HER2, 611-p95HER2 has negligible normal tissue expression. There is currently no approved diagnostic assay to identify this subgroup and no therapy targeting this mechanism of tumor escape. The purpose of this study was to develop a monoclonal antibody (mAb) against 611-CTF-p95HER2. Hybridomas were generated from rats immunized with cells expressing 611-CTF. A hybridoma producing a highly specific Ab was identified and cloned further as a mAb. This mAb, called Oslo-2, gave strong staining for 611-CTF and no binding to full-length HER2, as assessed in cell lines and tissues by flow cytometry, immunohistochemistry and immunofluorescence. No cross-reactivity against HER2 negative controls was detected. Surface plasmon resonance analysis demonstrated a high binding affinity (equilibrium dissociation constant 2 nM). The target epitope was identified at the N-terminal end, using experimental alanine scanning. Further, the mAb paratope was identified and characterized with hydrogen-deuterium-exchange, and a molecular model for the (Oslo-2 mAb:611-CTF-p95HER2) complex was generated by an experimental-information-driven docking approach. We conclude that the Oslo-2 mAb has a high affinity and is highly specific for 611-CTF-p95HER2. The Ab may be used to develop potent and safe therapies, overcoming p95HER2-mediated tumor escape, as well as for developing diagnostic assays.
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The prolonged transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus in the human population has led to demographic divergence and the emergence of several location-specific clusters of viral strains. Although the effect of mutation(s) on severity and survival of the virus is still unclear, it is evident that certain sites in the viral proteome are more/less prone to mutations. In fact, millions of SARS-CoV-2 sequences collected all over the world have provided us a unique opportunity to understand viral protein mutations and develop novel computational approaches to predict mutational patterns. In this study, we have classified the mutation sites into low and high mutability classes based on viral isolates count containing mutations. The physicochemical features and structural analysis of the SARS-CoV-2 proteins showed that features including residue type, surface accessibility, residue bulkiness, stability and sequence conservation at the mutation site were able to classify the low and high mutability sites. We further developed machine learning models using above-mentioned features, to predict low and high mutability sites at different selection thresholds (ranging 5-30% of topmost and bottommost mutated sites) and observed the improvement in performance as the selection threshold is reduced (prediction accuracy ranging from 65 to 77%). The analysis will be useful for early detection of variants of concern for the SARS-CoV-2, which can also be applied to other existing and emerging viruses for another pandemic prevention.
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COVID-19 , SARS-CoV-2 , COVID-19/genética , Genoma Viral , Humanos , Mutação/genética , Pandemias , Proteoma/genética , SARS-CoV-2/genéticaRESUMO
SUMMARY: We have developed a database, Ab-CoV, which contains manually curated experimental interaction profiles of 1780 coronavirus-related neutralizing antibodies. It contains more than 3200 datapoints on half maximal inhibitory concentration (IC50), half maximal effective concentration (EC50) and binding affinity (KD). Each data with experimentally known three-dimensional structures are complemented with predicted change in stability and affinity of all possible point mutations of interface residues. Ab-CoV also includes information on epitopes and paratopes, structural features of viral proteins, sequentially similar therapeutic antibodies and Collier de Perles plots. It has the feasibility for structure visualization and options to search, display and download the data. AVAILABILITY AND IMPLEMENTATION: Ab-CoV database is freely available at https://web.iitm.ac.in/bioinfo2/ab-cov/home. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Anticorpos Antivirais , Coronavirus , Anticorpos Antivirais/química , Anticorpos Neutralizantes/química , Glicoproteína da Espícula de Coronavírus/química , Bases de Dados FactuaisRESUMO
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
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Antineoplásicos Imunológicos , Inteligência Artificial , Algoritmos , Anticorpos Monoclonais/uso terapêutico , Aprendizado de MáquinaRESUMO
The coronavirus disease 2019 (COVID-19) has affected the lives of millions of people around the world. In an effort to develop therapeutic interventions and control the pandemic, scientists have isolated several neutralizing antibodies against SARS-CoV-2 from the vaccinated and convalescent individuals. These antibodies can be explored further to understand SARS-CoV-2 specific antigen-antibody interactions and biophysical parameters related to binding affinity, which can be utilized to engineer more potent antibodies for current and emerging SARS-CoV-2 variants. In the present study, we have analyzed the interface between spike protein of SARS-CoV-2 and neutralizing antibodies in terms of amino acid residue propensity, pair preference, and atomic interaction energy. We observed that Tyr residues containing contacts are highly preferred and energetically favorable at the interface of spike protein-antibody complexes. We have also developed a regression model to relate the experimental binding affinity for antibodies using structural features, which showed a correlation of 0.93. Moreover, several mutations at the spike protein-antibody interface were identified, which may lead to immune escape (epitope residues) and improved affinity (paratope residues) in current/emerging variants. Overall, the work provides insights into spike protein-antibody interactions, structural parameters related to binding affinity and mutational effects on binding affinity change, which can be helpful to develop better therapeutics against COVID-19.
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Anticorpos Neutralizantes/imunologia , COVID-19/imunologia , SARS-CoV-2/imunologia , Glicoproteína da Espícula de Coronavírus/imunologia , Anticorpos Neutralizantes/química , Sítios de Ligação de Anticorpos , Epitopos/química , Epitopos/imunologia , Humanos , Simulação de Acoplamento Molecular , SARS-CoV-2/química , Glicoproteína da Espícula de Coronavírus/químicaRESUMO
Aggregation of therapeutic monoclonal antibodies (mAbs) can negatively affect their chemistry, manufacturing, and control attributes and lead to undesirable immune responses in patients. Therefore, optimization of lead mAb drug candidates during discovery stages to mitigate aggregation is increasingly becoming an integral part of their developability assessments. The disruption of short sequence motifs called aggregation prone regions (APRs) found in amino acid sequences of mAb candidates can potentially mitigate their aggregation. In this work, we have performed molecular dynamics simulations to study the aggregation of an APR (VLVIY) found in λ light chains of human antibodies and its single point mutant KLVIY. Eighteen different multicopy peptide simulation systems of "VLVIY" and "KLVIY" were constructed by varying their concentrations, temperatures, termini capping, and flanking gate-keeper regions. Within 20 ns of the simulation, peptide "VLVIY" formed an aggregate of 100 peptides at ~0.1 M concentration with a 60% reduction in solvent accessible surface area (SASA). Furthermore, analysis of the SASA change, peptide cluster distribution, and water residence time demonstrated how Val â Lys mutation resists aggregation and improves solubility. Presence of Lys slows down aggregation kinetics via charge-charge repulsions and by raising the kinetic barrier to formation of large oligomers. However, the effect of the Val â Lys mutation is dependent on sequence and structural contexts around the APR. This mutation also alters the solvation shell around the peptide by favoring solute-solvent interactions, thereby increasing its solubility. This work has provided a detailed mechanistic explanation of how APR disruption can mitigate aggregation in biotherapeutics and improve their developability.
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Peptídeos/química , Anticorpos Monoclonais , Humanos , Simulação de Dinâmica Molecular , Agregados ProteicosRESUMO
Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.
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Anticorpos , Reações Antígeno-Anticorpo , Especificidade de Anticorpos , Epitopos/química , Aprendizado de MáquinaRESUMO
Mitigating the devastating effect of COVID-19 is necessary to control the infectivity and mortality rates. Hence, several strategies such as quarantine of exposed and infected individuals and restricting movement through lockdown of geographical regions have been implemented in most countries. On the other hand, standard SEIR based mathematical models have been developed to understand the disease dynamics of COVID-19, and the proper inclusion of these restrictions is the rate-limiting step for the success of these models. In this work, we have developed a hybrid Susceptible-Exposed-Infected-Quarantined-Removed (SEIQR) model to explore the influence of quarantine and lockdown on disease propagation dynamics. The model is multi-compartmental, and it considers everyday variations in lockdown regulations, testing rate and quarantine individuals. Our model predicts a considerable difference in reported and actual recovered and deceased cases in qualitative agreement with recent reports.
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COVID-19/prevenção & controle , Humanos , Modelos Teóricos , Quarentena , Processos EstocásticosRESUMO
The light chain (AL) amyloidosis is caused by the aggregation of light chain of antibodies into amyloid fibrils. There are plenty of computational resources available for the prediction of short aggregation-prone regions within proteins. However, it is still a challenging task to predict the amyloidogenic nature of the whole protein using sequence/structure information. In the case of antibody light chains, common architecture and known binding sites can provide vital information for the prediction of amyloidogenicity at physiological conditions. Here, in this work, we have compared classical sequence-based, aggregation-related features (such as hydrophobicity, presence of gatekeeper residues, disorderness, ß-propensity, etc.) calculated for the CDR, FR or VL regions of amyloidogenic and non-amyloidogenic antibody light chains and implemented the insights gained in a machine learning-based webserver called "VLAmY-Pred" ( https://web.iitm.ac.in/bioinfo2/vlamy-pred/ ). The model shows prediction accuracy of 79.7% (sensitivity: 78.7% and specificity: 79.9%) with a ROC value of 0.88 on a dataset of 1828 variable region sequences of the antibody light chains. This model will be helpful towards improved prognosis for patients that may likely suffer from diseases caused by light chain amyloidosis, understanding origins of aggregation in antibody-based biotherapeutics, large-scale in-silico analysis of antibody sequences generated by next generation sequencing, and finally towards rational engineering of aggregation resistant antibodies.
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Amiloide/genética , Cadeias Leves de Imunoglobulina/genética , Amiloidose de Cadeia Leve de Imunoglobulina/genética , Agregação Patológica de Proteínas/genética , Sequência de Aminoácidos/genética , Amiloide/química , Amiloide/imunologia , Amiloide/ultraestrutura , Biologia Computacional , Humanos , Interações Hidrofóbicas e Hidrofílicas , Cadeias Leves de Imunoglobulina/química , Cadeias Leves de Imunoglobulina/imunologia , Cadeias Leves de Imunoglobulina/ultraestrutura , Amiloidose de Cadeia Leve de Imunoglobulina/imunologia , Amiloidose de Cadeia Leve de Imunoglobulina/patologia , Modelos Moleculares , Agregação Patológica de Proteínas/patologia , Conformação ProteicaRESUMO
Protein aggregation has two aspects, namely, mechanistic and kinetics. Understanding protein aggregation kinetics is critical for prediction of progression of diseases caused by amyloidosis, accumulation of aggregates in biotherapeutics during storage and engineering commercial nano-biomaterials. In this work, we have collected experimentally determined absolute protein aggregation rates and developed an SVM based regression model to predict absolute rates of protein and peptide aggregation near-physiological conditions. The regression model achieved a correlation coefficient of 0.72 with MAE of 0.91 (natural log of kapp, where kapp is in hour-1) using leave-one-out cross-validation on a dataset of 82 non-redundant proteins/peptides. The model accounts for the experimental conditions (such as temperature, pH, ionic and protein concentration) and sequence-based properties. The amino acid sequence features revealed by this model as being important for aggregation kinetics, are also associated with the aggregation mechanism. In particular, inherent aggregation propensity of the protein/peptide sequence and number of aggregation prone regions (APRs) unpunctuated by the gatekeeping residues, were found to play important roles in the prediction of the absolute aggregation rates. This analysis shows that mechanism and kinetics of protein aggregation are coupled via common sequence attributes. The aggregation kinetic prediction method developed in this work is available at https://web.iitm.ac.in/bioinfo2/absolurate-pred/index.html.
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Biologia Computacional/métodos , Previsões/métodos , Agregados Proteicos/fisiologia , Algoritmos , Amiloide/química , Simulação por Computador , Bases de Dados de Proteínas , Cinética , Modelos Químicos , Peptídeos/química , Proteínas/química , Análise de RegressãoRESUMO
Several prediction algorithms and tools have been developed in the last two decades to predict protein and peptide aggregation. These in silico tools aid to predict the aggregation propensity and amyloidogenicity as well as the identification of aggregation-prone regions. Despite the immense interest in the field, it is of prime importance to systematically compare these algorithms for their performance. In this review, we have provided a rigorous performance analysis of nine prediction tools using a variety of assessments. The assessments were carried out on several non-redundant datasets ranging from hexapeptides to protein sequences as well as amyloidogenic antibody light chains to soluble protein sequences. Our analysis reveals the robustness of the current prediction tools and the scope for improvement in their predictive performances. Insights gained from this work provide critical guidance to the scientific community on advantages and limitations of different aggregation prediction methods and make informed decisions about their research needs.
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Biologia Computacional/métodos , Bases de Dados de Proteínas , Peptídeos/metabolismo , Agregação Patológica de Proteínas/metabolismo , Proteínas/metabolismo , Algoritmos , Sequência de Aminoácidos , Proteínas Amiloidogênicas/química , Proteínas Amiloidogênicas/metabolismo , Humanos , Peptídeos/química , Agregação Patológica de Proteínas/etiologia , Ligação Proteica , Proteínas/química , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Relação Estrutura-Atividade , NavegadorRESUMO
The urgent need for a treatment of COVID-19 has left researchers with limited choice of either developing an effective vaccine or identifying approved/investigational drugs developed for other medical conditions for potential repurposing, thus bypassing long clinical trials. In this work, we compared the sequences of experimentally verified SARS-CoV-2 neutralizing antibodies and sequentially/structurally similar commercialized therapeutic monoclonal antibodies. We have identified three therapeutic antibodies, Tremelimumab, Ipilimumab and Afasevikumab. Interestingly, these antibodies target CTLA4 and IL17A, levels of which have been shown to be elevated during severe SARS-CoV-2 infection. The candidate antibodies were evaluated further for epitope restriction, interaction energy and interaction surface to gauge their repurposability to tackle SARS-CoV-2 infection. Our work provides candidate antibody scaffolds with dual activities of plausible viral neutralization and immunosuppression. Further, these candidate antibodies can also be explored in diagnostic test kits for SARS-CoV-2 infection. We opine that this in silico workflow to screen and analyze antibodies for repurposing would have widespread applications.
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Anticorpos Monoclonais/farmacologia , Anticorpos Neutralizantes/farmacologia , Tratamento Farmacológico da COVID-19 , Reposicionamento de Medicamentos , Anticorpos Monoclonais/imunologia , Anticorpos Monoclonais Humanizados/imunologia , Anticorpos Monoclonais Humanizados/farmacologia , Anticorpos Neutralizantes/imunologia , COVID-19/imunologia , Reposicionamento de Medicamentos/métodos , Epitopos/imunologia , Humanos , Ipilimumab/imunologia , Ipilimumab/farmacologia , Simulação de Acoplamento Molecular , SARS-CoV-2/efeitos dos fármacos , SARS-CoV-2/imunologia , Glicoproteína da Espícula de Coronavírus/imunologiaRESUMO
Short aggregation prone sequence motifs can trigger aggregation in peptide and protein sequences. Most algorithms developed so far to identify potential aggregation prone regions (APRs) use amino acid residue composition and/or sequence pattern features. In this work, we have investigated the importance of atomic-level characteristics rather than residue level to understand the initiation of aggregation in proteins and peptides. Using atomic-level features an ensemble-classifier, ANuPP has been developed to predict the aggregation-nucleating regions in peptides and proteins. In a dataset of 1279 hexapeptides, ANuPP achieved an area under the curve (AUC) of 0.831 with 77% accuracy on 10-fold cross-validation and an AUC of 0.883 with 83% accuracy in a blind test dataset of 142 hexapeptides. Further, it showed an average SOV of 48.7% on identifying APR regions in 37 proteins. The performance of ANuPP is better than other methods reported in the literature on both amyloidogenic hexapeptide prediction and APR identification. We have developed a web server for ANuPP and it is available at https://web.iitm.ac.in/bioinfo2/ANuPP/. Insights gained from this work demonstrate the importance of atomic and functional group characteristics towards diversity of atomic level origins as well as mechanisms of protein aggregation.
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Algoritmos , Peptídeos/química , Agregados Proteicos , Proteínas/química , Amiloide/química , Bases de Dados de Proteínas , Interações Hidrofóbicas e HidrofílicasRESUMO
Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.
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Coronaviruses are responsible for several epidemics, including the 2002 SARS, 2012 MERS, and COVID-19. The emergence of recent COVID-19 pandemic due to SARS-CoV-2 virus in December 2019 has resulted in considerable research efforts to design antiviral drugs and other therapeutics against coronaviruses. In this context, it is crucial to understand the biophysical and structural features of the major proteins that are involved in virus-host interactions. In the current study, we have compared spike proteins from three strains of coronaviruses NL63, SARS-CoV, and SARS-CoV, known to bind human angiotensin-converting enzyme 2 (ACE2), in terms of sequence/structure conservation, hydrophobic cluster formation and importance of binding site residues. The study reveals that the severity of coronavirus strains correlates positively with the interaction area, surrounding hydrophobicity and interaction energy and inversely correlate with the flexibility of the binding interface. Also, we identify the conserved residues in the binding interface of spike proteins in all three strains. The systematic point mutations show that these conserved residues in the respective strains are evolutionarily favored at their respective positions. The similarities and differences in the spike proteins of the three viruses indicated in this study may help researchers to deeply understand the structural behavior, binding site properties and etiology of ACE2 binding, accelerating the screening of potential lead molecules and the development/repurposing of therapeutic drugs.