Calendario para el Reyno de Valencia...: Año 1902

Microfilme. Valencia : BV, ca. 1990Recurso electrónico. Valencia : BVNP, 20141902_A_95667

Toward real-world automated antibody design with combinatorial Bayesian optimization

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

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

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

Validating a Genomic Convergence and Network Analysis Approach Using Association Analysis of Identified Candidate Genes in Alzheimer’s Disease

Previously, we demonstrated an integrated genomic convergence and network analysis approach to identify the candidate genes associated with the complex neurodegenerative disorder, Alzheimer's disease (AD). Here, we performed a pilot study to validate the in silico approach by studying the association of genetic variants from three identified critical genes, APOE, EGFR, and ACTB, with AD. A total of 103 patients with AD and 146 healthy controls were recruited. A total of 46 single-nucleotide polymorphisms (SNPs) spanning the three genes were genotyped, of which only 19 SNPs were included in the final analyses after excluding non-polymorphic and Hardy-Weinberg equilibrium-violating SNPs. Apart from our previously reported APOE ε4, four other SNPs in APOE (rs405509, rs7259620, -rs769449, and rs7256173), one in EGFR (rs6970262), and one in ACTB (rs852423) showed a significant association with AD (p < 0.05). Our results validate the reliability of genomic convergence and network analysis approach in identifying the AD-associated candidate genes

A systematic review and integrative approach to decode the common molecular link between levodopa response and Parkinson’s disease

Abstract Background PD is a progressive neurodegenerative disorder commonly treated by levodopa. The findings from genetic studies on adverse effects (ADRs) and levodopa efficacy are mostly inconclusive. Here, we aim to identify predictive genetic biomarkers for levodopa response (LR) and determine common molecular link with disease susceptibility. A systematic review for LR was conducted for ADR, and drug efficacy, independently. All included articles were assessed for methodological quality on 14 parameters. GWAS of PD were also reviewed. Protein-protein interaction (PPI) analysis using STRING and functional enrichment using WebGestalt was performed to explore the common link between LR and PD. Results From 37 candidate studies on levodopa toxicity, 18 genes were found associated, of which, CAn STR 13, 14 (DRD2) was most significantly associated with dyskinesia, followed by rs1801133 (MTHFR) with hyper-homocysteinemia, and rs474559 (HOMER1) with hallucination. Similarly, 8 studies on efficacy resulted in 4 genes in which rs28363170, rs3836790 (SLC6A3) and rs4680 (COMT), were significant. To establish the molecular connection between LR with PD, we identified 35 genes significantly associated with PD. With 19 proteins associated with LR and 35 with PD, two independent PPI networks were constructed. Among the 67 nodes (263 edges) in LR, and 62 nodes (190 edges) in PD pathophysiology, UBC, SNCA, FYN, SRC, CAMK2A, and SLC6A3 were identified as common potential candidates. Conclusion Our study revealed the genetically significant polymorphism concerning the ADRs and levodopa efficacy. The six common genes may be used as predictive markers for therapy optimization and as putative drug target candidates

Adaptive immune receptor repertoire analysis

B cell and T cell receptor repertoires compose the adaptive immune receptor repertoire (AIRR) of an individual. The AIRR is a unique collection of antigen-specific receptors that drives adaptive immune responses, which in turn is imprinted in each individual AIRR. This supports the concept that the AIRR could determine disease outcomes, for example in autoimmunity, infectious disease and cancer. AIRR analysis could therefore assist the diagnosis, prognosis and treatment of human diseases towards personalized medicine. High-throughput sequencing, high-dimensional statistical analysis, computational structural biology and machine learning are currently employed to study the shaping and dynamics of the AIRR as a function of time and antigenic challenges. This Primer provides an overview of concepts and state-of-the-art methods that underlie experimental and computational AIRR analysis and illustrates the diversity of relevant applications. The Primer also addresses some of the outstanding challenges in AIRR analysis, such as sampling, sequencing depth, experimental variations and computational biases, while discussing prospects of future AIRR analysis applications for understanding and predicting adaptive immune responses

Development of a High-Affinity Antibody against the Tumor-Specific and Hyperactive 611-p95HER2 Isoform

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