scholarly journals Mixture of Experts for Predicting Antibody-Antigen Binding Affinity from Antigen Sequence

2019 ◽  
Author(s):  
Srivamshi Pittala ◽  
Chris Bailey-Kellogg
Keyword(s):  
Binding Affinity ◽  
Large Scale ◽  
Vaccine Development ◽  
Predictive Accuracy ◽  
Scale Up ◽  
Antigen Binding ◽  
Mixture Of Experts ◽  
Large Sets ◽  
Wide Range ◽  
Antigen Variant

AbstractAntibodies provide a key mode of defense employed by the immune system to fight disease, so eliciting potent antibodies is one of the main goals in vaccine development. Antibodies are also being conceived as powerful therapeutic agents, so engineering potent antibodies is one of the main goals in biologic drug development. The power of antibodies lies in their affinity and specificity in recognizing their cognate antigens. Unfortunately, experimental techniques to determine antibody-antigen binding affinities are difficult to scale up to large sets of new antibodies and antigens. Though computational methods are suitable for large-scale prediction, current methods lack sufficient accuracy. Here, we address the problem of predicting the binding affinity of an antibody against an antigen variant based on the amino acid sequence of that antigen variant. We develop a mixture of experts approach that learns models for individual antibodies against some antigen variants, and then combines information across the antibodies in order to make accurate predictions for a wide range of new variants. In evaluation on a dataset consisting of 52 antibodies and 608 strains of HIV, the predictive accuracy of our approach is demonstrated to be significantly better than that of an existing approach. Our method provides a fast and accurate way to predict antibody-antigen binding affinity, which has the potential to expedite the study of antibody-antigen interactions for vaccine design and therapy.

scholarly journals BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space

2021 ◽  
Vol 22 (15) ◽  
pp. 7773
Author(s):  
Neann Mathai ◽  
Conrad Stork ◽  
Johannes Kirchmair
Keyword(s):  
Large Scale ◽  
Computational Approach ◽  
Large Sets ◽  
Compound Libraries ◽  
Wide Range ◽  
Protein Space ◽  
High Chance ◽  
Large Scale Screening ◽  
Early Drug ◽  
Selection Of

Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the “fitness” of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle (“BonMOLière”).

scholarly journals Insights into Cross-species Evolution of Novel Human Coronavirus 2019-nCoV and Defining Immune Determinants for Vaccine Development

2020 ◽  
Author(s):  
Arunachalam Ramaiah ◽  
Vaithilingaraja Arumugaswami
Keyword(s):  
Binding Affinity ◽  
Vaccine Development ◽  
Asia Pacific ◽  
Potential Candidate ◽  
T Cell Epitopes ◽  
Live Animal ◽  
Synonymous Mutations ◽  
Wide Range ◽  
Novel Coronavirus ◽  
N Proteins

ABSTRACTNovel Coronavirus (nCoV) outbreak in the city of Wuhan, China during December 2019, has now spread to various countries across the globe triggering a heightened containment effort. This human pathogen is a member of betacoronavirus genus carrying 30 kilobase of single positive-sense RNA genome. Understanding the evolution, zoonotic transmission, and source of this novel virus would help accelerating containment and prevention efforts. The present study reported detailed analysis of 2019-nCoV genome evolution and potential candidate peptides for vaccine development. This nCoV genotype might have been evolved from a bat-CoV by accumulating non-synonymous mutations, indels, and recombination events. Structural proteins Spike (S), and Membrane (M) had extensive mutational changes, whereas Envelope (E) and Nucleocapsid (N) proteins were very conserved suggesting differential selection pressures exerted on 2019-nCoV during evolution. Interestingly, 2019-nCoV Spike protein contains a 39 nucleotide sequence insertion relative to SARS-like bat-SL-CoVZC45/2017. Furthermore, we identified eight high binding affinity (HBA) CD4 T-cell epitopes in the S, E, M and N proteins, which can be commonly recognized by HLA-DR alleles of Asia and Asia-Pacific Region population. These immunodominant epitopes can be incorporated in universal subunit CoV vaccine. Diverse HLA types and variations in the epitope binding affinity may contribute to the wide range of immunopathological outcomes of circulating virus in humans. Our findings emphasize the requirement for continuous surveillance of CoV strains in live animal markets to better understand the viral adaptation to human host and to develop practical solutions to prevent the emergence of novel pathogenic CoV strains.

scholarly journals Transplantation of Human Fetal-Derived Neural Stem Cells Improves Cognitive Function following Cranial Irradiation

2014 ◽  
Vol 23 (10) ◽  
pp. 1255-1266 ◽  
Author(s):  
Munjal M. Acharya ◽  
Lori-Ann Christie ◽  
Thomas G. Hazel ◽  
Karl K. Johe ◽  
Charles L. Limoli
Keyword(s):  
Stem Cells ◽  
Cognitive Deficits ◽  
Large Scale ◽  
Scale Up ◽  
Progression Free Survival ◽  
Cranial Irradiation ◽  
Contextual Fear Conditioning ◽  
Human Stem Cells ◽  
Cranial Radiotherapy ◽  
Wide Range

Treatment of central nervous system (CNS) malignancies typically involves radiotherapy to forestall tumor growth and recurrence following surgical resection. Despite the many benefits of cranial radiotherapy, survivors often suffer from a wide range of debilitating and progressive cognitive deficits. Thus, while patients afflicted with primary and secondary malignancies of the CNS now experience longer local regional control and progression-free survival, there remains no clinical recourse for the unintended neurocognitive sequelae associated with their cancer treatments. Multiple mechanisms contribute to disrupted cognition following irradiation, including the depletion of radiosensitive populations of stem and progenitor cells in the hippocampus. We have explored the potential of using intrahippocampal transplantation of human stem cells to ameliorate radiation-induced cognitive dysfunction. Past studies demonstrated the capability of cranially transplanted human embryonic (hESCs) and neural (hNSCs) stem cells to functionally restore cognition in rats 1 and 4 months after cranial irradiation. The present study employed an FDA-approved fetal-derived hNSC line capable of large scale-up under good manufacturing practice (GMP). Animals receiving cranial transplantation of these cells 1 month following irradiation showed improved hippocampal spatial memory and contextual fear conditioning performance compared to irradiated, sham surgery controls. Significant newly born (doublecortin positive) neurons and a smaller fraction of glial subtypes were observed within and nearby the transplantation core. Engrafted cells migrated and differentiated into neuronal and glial subtypes throughout the CA1 and CA3 subfields of the host hippocampus. These studies expand our prior findings to demonstrate that transplantation of fetal-derived hNSCs improves cognitive deficits in irradiated animals, as assessed by two separate cognitive tasks.

scholarly journals Scalable Production of Monodisperse Functional Microspheres by Multilayer Parallelization of High Aspect Ratio Microfluidic Channels

Micromachines ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 592 ◽  
Author(s):  
Casper Ho Yin Chung ◽  
Binbin Cui ◽  
Ruyuan Song ◽  
Xin Liu ◽  
Xiaonan Xu ◽  
...  
Keyword(s):  
Aspect Ratio ◽  
High Aspect Ratio ◽  
Large Scale ◽  
Scale Up ◽  
Functional Materials ◽  
Droplet Microfluidics ◽  
Microfluidic Channels ◽  
Layer By Layer ◽  
Precise Control ◽  
Wide Range

Droplet microfluidics enables the generation of highly uniform emulsions with excellent stability, precise control over droplet volume, and morphology, which offer superior platforms over conventional technologies for material synthesis and biological assays. However, it remains a challenge to scale up the production of the microfluidic devices due to their complicated geometry and long-term reliability. In this study, we present a high-throughput droplet generator by parallelization of high aspect ratio rectangular structures, which enables facile and scalable generation of uniform droplets without the need to precisely control external flow conditions. A multilayer device is formed by stacking layer-by-layer of the polydimethylsiloxane (PDMS) replica patterned with parallelized generators. By feeding the sample fluid into the device immersed in the carrying fluid, we used the multilayer device with 1200 parallelized generators to generate monodisperse droplets (~45 μm in diameter with a coefficient of variation <3%) at a frequency of 25 kHz. We demonstrate this approach is versatile for a wide range of materials by synthesis of polyacrylamide hydrogel and Poly (l-lactide-co-glycolide) (PLGA) through water-in-oil (W/O) and oil-in-water (O/W) emulsion templates, respectively. The combined scalability and robustness of such droplet emulsion technology is promising for production of monodisperse functional materials for large-scale applications.

scholarly journals PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Tingting Sun ◽  
Yuting Chen ◽  
Yuhao Wen ◽  
Zefeng Zhu ◽  
Minghui Li
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Large Scale ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Driver Mutations ◽  
Missense Mutations ◽  
Resistance Mutations ◽  
Benchmark Datasets

AbstractResistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug resistance. Computational methods have made a lot of progress for predicting binding affinity changes and identifying resistance mutations, but their prediction accuracy and speed are still not satisfied and need to be further improved. To address these issues, we introduce a structure-based machine learning method for quantitatively estimating the effects of single mutations on ligand binding affinity changes (named as PremPLI). A comprehensive comparison of the predictive performance of PremPLI with other available methods on two benchmark datasets confirms that our approach performs robustly and presents similar or even higher predictive accuracy than the approaches relying on first-principle statistical mechanics and mixed physics- and knowledge-based potentials while requires much less computational resources. PremPLI can be used for guiding the design of ligand-binding proteins, identifying and understanding disease driver mutations, and finding potential resistance mutations for different drugs. PremPLI is freely available at https://lilab.jysw.suda.edu.cn/research/PremPLI/ and allows to do large-scale mutational scanning.

The Necessity for Scale Up in R&D: Approach for Waste Immobilisation in Cement by BNFL

1995 ◽  
Vol 412 ◽  
Author(s):  
Graham A Fairhall ◽  
Eric W Miller
Keyword(s):  
Material Properties ◽  
Large Scale ◽  
Scale Up ◽  
Scaling Up ◽  
Full Scale ◽  
Physical Parameters ◽  
Wide Range ◽  
Regulatory Bodies ◽  
Major Factors

AbstractThe full scale processing of nuclear wastes immobilised in cement utilises a wide range of chemical and physical parameters. The success of this work however, involves many factors and material properties which are affected by the actual scaling up processes. The paper outlines the approach and experience gained by BNFL to recognise and evaluate the major factors involved in order to successfully produce large scale stable products acceptable to the appropriate regulatory bodies and suitable for long term disposal.

scholarly journals PremPLI: Predicting the Effects of Missense Mutations on Protein-Ligand Interactions

2021 ◽  
Author(s):  
Tingting Sun ◽  
Yuting Chen ◽  
Yuhao Wen ◽  
Zefeng Zhu ◽  
Minghui Li
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Large Scale ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Driver Mutations ◽  
Missense Mutations ◽  
Resistance Mutations ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Abstract Protein-ligand interactions trigger a multitude of signal transduction processes and resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug resistance. Computational methods have made a lot of progress for predicting binding affinity changes and identifying resistance mutations, but they are still not satisfied and need to be further improved in both accuracy and speed. To address these issues, we introduced PremPLI, a structure-based machine learning method for quantitatively estimating the effects of single mutations on ligand binding affinity changes. A comprehensive comparison of the predictive performance of PremPLI with other available methods on two benchmark datasets confirms that our approach performs robustly and presents similar or even higher predictive accuracy than the approaches relying on first-principle statistical mechanics and mixed physics- and knowledge-based potentials while requires much less computational resources. PremPLI can be used for guiding the design of ligand-binding proteins, identifying and understanding disease driver mutations, and finding potential resistance mutations for different drugs. PremPLI is freely available at https://lilab.jysw.suda.edu.cn/research/PremPLI/ and allows to do large-scale mutational scanning.

Large Scale Production of Nanoparticles by Laser Pyrolysis

2007 ◽  
Vol 534-536 ◽  
pp. 85-88 ◽  
Author(s):  
Adrien Reau ◽  
Benoit Guizard ◽  
Cyrille Mengeot ◽  
Loic Boulanger ◽  
François Ténégal
Keyword(s):  
Large Scale ◽  
Gas Flow ◽  
Laser Intensity ◽  
Scale Up ◽  
Pilot Scale ◽  
Scale Production ◽  
Laser Pyrolysis ◽  
Quenching Effect ◽  
Wide Range ◽  
The Mean

Laser pyrolysis is a very suitable gas-phase process for the synthesis of a wide range of nanoparticles at laboratory scale. The principle of the method is based on the decomposition of gaseous or liquid reactants by a high power CO2 laser followed by a quenching effect. The literature reports the possibility to produce carbides, nitrides, oxides, metals and composites nanoparticles by this process. This paper reports a study of the effect of the laser intensity (using an innovative optical system) and of the gas flow rates on the characteristics (size and structure) of silicon carbide (SiC) nanoparticles produced at pilot scale (up to 1.13 kg/h) by using a mixture of silane (SiH4) and acetylene (C2H2). It has been shown that the decrease of the gas flow rate favors the increase of the mean grain size of the particles and that the increase of the laser intensity seems to provoke an increase of the mean crystal size and/or crystal number.

scholarly journals Native Folding of a Recombinant gpE1/gpE2 Heterodimer Vaccine Antigen from a Precursor Protein Fused with Fc IgG

2016 ◽  
Vol 91 (1) ◽  
Author(s):  
Michael Logan ◽  
John Law ◽  
Jason Alexander Ji-Xhin Wong ◽  
Darren Hockman ◽  
Amir Landi ◽  
...  
Keyword(s):  
Neutralizing Antibodies ◽  
Large Scale ◽  
Vaccine Development ◽  
Vaccine Candidate ◽  
Affinity Purification ◽  
Scale Up ◽  
Vaccine Antigen ◽  
Scale Production ◽  
Genotype 1A ◽  
Neutralizing Epitopes

ABSTRACT A recombinant strain HCV1 (hepatitis C virus [HCV] genotype 1a) gpE1/gpE2 (E1E2) vaccine candidate was previously shown by our group to protect chimpanzees and generate broad cross-neutralizing antibodies in animals and humans. In addition, recent independent studies have highlighted the importance of conserved neutralizing epitopes in HCV vaccine development that map to antigenic clusters in E2 or the E1E2 heterodimer. E1E2 can be purified using Galanthis nivalis lectin agarose (GNA), but this technique is suboptimal for global production. Our goal was to investigate a high-affinity and scalable method for isolating E1E2. We generated an Fc tag-derived (Fc-d) E1E2 that was selectively captured by protein G Sepharose, with the tag being removed subsequently using PreScission protease. Surprisingly, despite the presence of the large Fc tag, Fc-d E1E2 formed heterodimers similar to those formed by GNA-purified wild-type (WT) E1E2 and exhibited nearly identical binding profiles to HCV monoclonal antibodies that target conserved neutralizing epitopes in E2 (HC33.4, HC84.26, and AR3B) and the E1E2 heterodimer (AR4A and AR5A). Antisera from immunized mice showed that Fc-d E1E2 elicited anti-E2 antibody titers and neutralization of HCV pseudotype viruses similar to those with WT E1E2. Competition enzyme-linked immunosorbent assays (ELISAs) showed that antisera from immunized mice inhibited monoclonal antibody binding to neutralizing epitopes. Antisera from Fc-d E1E2-immunized mice exhibited stronger competition for AR3B and AR5A than the WT, whereas the levels of competition for HC84.26 and AR4A were similar. We anticipate that Fc-d E1E2 will provide a scalable purification and manufacturing process using protein A/G-based chromatography. IMPORTANCE A prophylactic HCV vaccine is still needed to control this global disease despite the availability of direct-acting antivirals. Previously, we demonstrated that a recombinant envelope glycoprotein (E1E2) vaccine (genotype 1a) elicited cross-neutralizing antibodies from human volunteers. A challenge for isolating the E1E2 antigen is the reliance on GNA, which is unsuitable for large scale-up and global vaccine delivery. We have generated a novel Fc domain-tagged E1E2 antigen that forms functional heterodimers similar to those with native E1E2. Affinity purification and removal of the Fc tag from E1E2 resulted in an antigen with a nearly identical profile of cross-neutralizing epitopes. This antigen elicited anti-HCV antibodies that targeted conserved neutralizing epitopes of E1E2. Owing to the high selectivity and cost-effective binding capacity of affinity resins for capture of the Fc-tagged rE1E2, we anticipate that our method will provide a means for large-scale production of this HCV vaccine candidate.

scholarly journals A Review on Multifunctional Carbon-Dots Synthesized From Biomass Waste: Design/ Fabrication, Characterization and Applications

2021 ◽  
Vol 9 ◽  
Author(s):  
Nurul Kamilah Khairol Anuar ◽  
Huey Ling Tan ◽  
Ying Pei Lim ◽  
Mohamad Sufian So’aib ◽  
Noor Fitrah Abu Bakar
Keyword(s):  
Carbon Dots ◽  
Metal Ion ◽  
Large Scale ◽  
Raw Materials ◽  
Low Cost ◽  
Agricultural Waste ◽  
Scale Up ◽  
Chemical Properties ◽  
Biomass Waste ◽  
Wide Range

Carbon-Dots (C-Dots) have drawn much attention in recent years owing to their remarkable properties such as high biocompatibility, low toxicity, nano-scale size, and ease of modification with good tuneable photoluminescence performance. These unique properties have led C-Dots to become a promising platform for bioimaging, metal ion sensing and an antibacterial agent. C-Dots can be prepared using the top-down and bottom-up approaches, in which the latter method is commonly used for large scale and low-cost synthesis. C-Dots can be synthesized using sustainable raw materials or green biomass since it is environmentally friendly, in-expensive and most importantly, promotes the minimization of waste production. However, using biomass waste to produce high-quality C-Dots is still a matter of concern waiting for resolution, and this will be the main focus of this review. Fundamental understanding of C-Dots such as structure analysis, physical and chemical properties of C-Dots, various synthesis methodology and type of raw materials used are also discussed and correlated comprehensively. Additionally, factors affecting the bandgap of the C-Dots and the strategies to overcome these shortcomings are also covered. Moreover, formation mechanism of C-Dots focusing on the hydrothermal method, option and challenges to scale up the C-Dots production are explored. It is expected that the great potential of producing C-Dots from agricultural waste a key benefit in view of their versatility in a wide range of applications.

Sign in / Sign up

Export Citation Format

Share Document