Creating novel proteins by combining design and selection.
Ontology highlight
ABSTRACT:
SUBMITTER: Grove TZ
PROVIDER: S-EPMC2865361 | biostudies-literature | 2010 Jun
REPOSITORIES: biostudies-literature
Publications
Creating novel proteins by combining design and selection.
Grove Tijana Z TZ Hands Michael M Regan Lynne L
Protein engineering, design & selection : PEDS 20100319 6
We present the results of combining design and selection to remodel a protein-peptide binding interface, using the peptide PTIEEVD and the TPR1 module interaction as our test case. We initially used the program Rosetta to interrogate possible TPR1 sequences compatible with binding the peptide PTIEEVD. Based on these results, we screened a small library of TPR1 variants, using a split GFP fluorescent assay to identify proteins that are able to bind to the PTIEEVD peptide. We discuss the similarit ...[more]
Similar Datasets
Project description:BackgroundHMF oxidase (HMFO) from Methylovorus sp. is a recently characterized flavoprotein oxidase. HMFO is a remarkable enzyme as it is able to oxidize 5-hydroxymethylfurfural (HMF) into 2,5-furandicarboxylic acid (FDCA): a catalytic cascade of three oxidation steps. Because HMF can be formed from fructose or other sugars and FDCA is a polymer building block, this enzyme has gained interest as an industrially relevant biocatalyst.ResultsTo increase the robustness of HMFO, a requirement for biotechnological applications, we decided to enhance its thermostability using the recently developed FRESCO method: a computational approach to identify thermostabilizing mutations in a protein structure. To make this approach even more effective, we now developed a new and facile gene shuffling approach to rapidly combine stabilizing mutations in a one-pot reaction. This allowed the identification of the optimal combination of seven beneficial mutations. The created thermostable HMFO mutant was further studied as a biocatalyst for the production of FDCA from HMF and was shown to perform significantly better than the original HMFO.ConclusionsThe described new gene shuffling approach quickly discriminates stable and active multi-site variants. This makes it a very useful addition to FRESCO. The resulting thermostable HMFO variant tolerates the presence of cosolvents and also remained thermotolerant after introduction of additional mutations aimed at improving the catalytic activity. Due to its stability and catalytic efficiency, the final HMFO variant appears to be a promising candidate for industrial scale production of FDCA from HMF.
| S-EPMC5831843 | biostudies-literature
Project description:Natural antibodies used as biorecognition elements suffer from numerous shortcomings, such as limited chemical and environmental stability and cost. Artificial antibodies based on molecular imprinting are an attractive alternative to natural antibodies. We investigated the role of aromatic interactions in target recognition capabilities of artificial antibodies. Three proteins with different aromatic amino acid content were employed as model targets. Artificial antibodies were formed on nanostructures using combinations of silane monomers of varying aromatic functionality. We employed refractive index sensitivity of plasmonic nanostructures as a transduction platform for monitoring various steps in the imprinting process and to quantify the target recognition capabilities of the artificial antibodies. The sensitivity of the artificial antibodies with aromatic interactions exhibited a protein-dependent enhancement. Selectivity and sensitivity enhancement due to the presence of aromatic groups in imprinted polymer matrix was found to be higher for target proteins with higher aromatic amino acid content. Our results indicate that tailoring the monomer composition based on the amino acid content of the target protein can improve the sensitivity of plasmonic biosensors based on artificial antibodies without affecting the selectivity.
| S-EPMC5372381 | biostudies-literature
Project description:The viability of chronic lymphocytic leukemia (CLL) is critically dependent upon staving off death by apoptosis, a hallmark of CLL pathophysiology. The recognition that Mcl-1, a major component of the anti-apoptotic response, is intrinsically short-lived and must be continually resynthesized suggested a novel therapeutic approach. Pateamine A (PatA), a macrolide marine natural product, inhibits cap-dependent translation by binding to the initiation factor eIF4A. In this study, we demonstrated that a synthetic derivative of PatA, des-methyl des-amino PatA (DMDAPatA), blocked mRNA translation, reduced Mcl-1 protein and initiated apoptosis in CLL cells. This action was synergistic with the Bcl-2 antagonist ABT-199. However, avid binding to human plasma proteins limited DMDAPatA potency, precluding further development. To address this, we synthesized a new series of PatA analogs and identified three new leads with potent inhibition of translation. They exhibited less plasma protein binding and increased cytotoxic potency toward CLL cells than DMDAPatA, with greater selectivity towards CLL cells over normal lymphocytes. Computer modeling analysis correlated their structure-activity relationships and suggested that these compounds may act by stabilizing the closed conformation of eIF4A. Thus, these novel PatA analogs hold promise for application to cancers within the appropriate biological context, such as CLL.
| S-EPMC8785363 | biostudies-literature
Project description:BACKGROUND:Gameful designs (gamification), using design pieces and concepts typically found in the world of games, is a promising approach to increase users' engagement with, and adherence to, electronic health and mobile health (mHealth) tools. Even though both identifying and addressing users' requirements and needs are important steps of designing information technology tools, little is known about the users' requirements and preferences for gameful designs in the context of self-management of chronic conditions. OBJECTIVE:This study aimed to present findings as well as the applied methods and design activities from a series of participatory design workshops with patients with chronic conditions, organized to generate and explore user needs, preferences, and ideas to the implementation of gameful designs in an mHealth self-management app. METHODS:We conducted three sets of two consecutive co-design workshops with a total of 22 participants with chronic conditions. In the workshops, we applied participatory design methods to engage users in different activities such as design games, scenario making, prototyping, and sticky notes exercises. The workshops were filmed, and the participants' interactions, written products, ideas, and suggestions were analyzed thematically. RESULTS:During the workshops, the participants identified a wide range of requirements, concerns, and ideas for using the gameful elements in the design of an mHealth self-management app. Overall inputs on the design of the app concerned aspects such as providing a positive user experience by promoting collaboration and not visibly losing to someone or by designing all feedback in the app to be uplifting and positive. The participants provided both general inputs (regarding the degree of competitiveness, use of rewards, or possibilities for customization) and specific inputs (such as being able to customize the look of their avatars or by having rewards that can be exchanged for real-world goods in a gift shop). However, inputs also highlighted the importance of making tools that provide features that are meaningful and motivating on their own and do not only have to rely on gameful design features to make people use them. CONCLUSIONS:The main contribution in this study was users' contextualized and richly described needs and requirements for gamefully designed mHealth tools for supporting chronic patients in self-management as well as the methods and techniques used to facilitate and support both the participant's creativity and communication of ideas and inputs. The range, variety, and depth of the inputs from our participants also showed the appropriateness of our design approach and activities. These findings may be combined with literature and relevant theories to further inform in the selection and application of gameful designs in mHealth apps, or they can be used as a starting point for conducting more participatory workshops focused on co-designing gameful health apps.
| S-EPMC6315237 | biostudies-literature
Project description:To ensure that new health information technology supports its intended users, researchers and developers need to follow human-centered methods during all stages of the software development lifecycle, including early stage evaluations. These evaluations need to include realistic testing scenarios to ensure that they provide valuable and accurate feedback to system developers. However, obtaining realistic patient data to support these evaluations has many challenges, including the risk of re-identifying anonymized patients as well as the costs associated with connecting test systems with production ready clinical databases. Here we present a novel five-step process to create highly structured and realistic synthetic patient data to support the evaluation and comparison of early to middle stage health information technology prototypes. We applied this method to evaluate and compare three novel health information technology prototypes designed to support clinicians during the identification of high-priority patients when answering the question: "What patient should I see first?" Our novel approach fills an important gap in the evaluation of health information technology and assists designers in creating high-quality software that best supports its end users.
| S-EPMC8120995 | biostudies-literature
Project description:BackgroundWheat is an important food crop globally, and timely prediction of wheat yield in breeding efforts can improve selection efficiency. Traditional yield prediction method based on secondary traits is time-consuming, costly, and destructive. It is urgent to develop innovative methods to improve selection efficiency and accelerate genetic gains in the breeding cycle.ResultsCrop yield prediction using remote sensing has gained popularity in recent years. This paper proposed a novel ensemble feature selection (EFS) method to improve yield prediction from hyperspectral data. For this, 207 wheat cultivars and breeding lines were grown under full and limited irrigation treatments respectively, and their canopy hyperspectral reflectance was measured at the flowering, early grain filling (EGF), mid grain filling (MGF), and late grain filling (LGF) stages. Then, 115 vegetation indices were extracted from the hyperspectral reflectance and combined with four feature selection methods, i.e., mean decrease impurity (MDI), Boruta, FeaLect, and RReliefF to train deep neural network (DNN) models for yield prediction. Next, a learning framework was developed by combining the predicted values of the selected and the full features using multiple linear regression (MLR). The results show that the selected features contributed to higher yield prediction accuracy than the full features, and the MDI method performed well across growth stages, with a mean R2 ranging from 0.634 to 0.666 (mean RMSE = 0.926-0.967 t ha-1). Also, the proposed EFS method outperformed all the individual feature selection methods across growth stages, with a mean R2 ranging from 0.648 to 0.679 (mean RMSE = 0.911-0.950 t ha-1).ConclusionsThe proposed EFS method can improve grain yield prediction from hyperspectral data and can be used to assist wheat breeders in earlier decision-making.
| S-EPMC9641855 | biostudies-literature
Project description:Activated factor XI (FXIa) inhibitors are anticipated to combine anticoagulant and profibrinolytic effects with a low bleeding risk. This motivated a structure aided fragment based lead generation campaign to create novel FXIa inhibitor leads. A virtual screen, based on docking experiments, was performed to generate a FXIa targeted fragment library for an NMR screen that resulted in the identification of fragments binding in the FXIa S1 binding pocket. The neutral 6-chloro-3,4-dihydro-1H-quinolin-2-one and the weakly basic quinolin-2-amine structures are novel FXIa P1 fragments. The expansion of these fragments towards the FXIa prime side binding sites was aided by solving the X-ray structures of reported FXIa inhibitors that we found to bind in the S1-S1'-S2' FXIa binding pockets. Combining the X-ray structure information from the identified S1 binding 6-chloro-3,4-dihydro-1H-quinolin-2-one fragment and the S1-S1'-S2' binding reference compounds enabled structure guided linking and expansion work to achieve one of the most potent and selective FXIa inhibitors reported to date, compound 13, with a FXIa IC50 of 1.0 nM. The hydrophilicity and large polar surface area of the potent S1-S1'-S2' binding FXIa inhibitors compromised permeability. Initial work to expand the 6-chloro-3,4-dihydro-1H-quinolin-2-one fragment towards the prime side to yield molecules with less hydrophilicity shows promise to afford potent, selective and orally bioavailable compounds.
| S-EPMC4309560 | biostudies-literature
Project description:The exponential growth in the volume of accessible biological information has generated a confusion of voices surrounding the annotation of molecular information about genes and their products. The Gene Ontology (GO) project seeks to provide a set of structured vocabularies for specific biological domains that can be used to describe gene products in any organism. This work includes building three extensive ontologies to describe molecular function, biological process, and cellular component, and providing a community database resource that supports the use of these ontologies. The GO Consortium was initiated by scientists associated with three model organism databases: SGD, the Saccharomyces Genome database; FlyBase, the Drosophila genome database; and MGD/GXD, the Mouse Genome Informatics databases. Additional model organism database groups are joining the project. Each of these model organism information systems is annotating genes and gene products using GO vocabulary terms and incorporating these annotations into their respective model organism databases. Each database contributes its annotation files to a shared GO data resource accessible to the public at http://www.geneontology.org/. The GO site can be used by the community both to recover the GO vocabularies and to access the annotated gene product data sets from the model organism databases. The GO Consortium supports the development of the GO database resource and provides tools enabling curators and researchers to query and manipulate the vocabularies. We believe that the shared development of this molecular annotation resource will contribute to the unification of biological information.
| S-EPMC311077 | biostudies-literature
Project description:Soft robotics is an emerging field in the robotics community which deals with completely new types of robots. However, often new soft robotic designs depend on the ingenuity of the engineer rather being systematically derived. For this reason, in order to support the engineer in the design process, we present a design methodology for general technical systems in this paper and explain it in depth in the context of soft robotics. The design methodology consists of a combination of state-of-the-art engineering concepts that are arranged in such a way that the engineer is guided through the design process. The effectiveness of a systematic approach in soft robotics is illustrated on the design of a new gecko-inspired, climbing soft robot.
| S-EPMC6208798 | biostudies-literature
Project description:A full understanding of the proteome will require ligands to all of the proteins encoded by genomes. While antibodies represent the principle affinity reagents used to bind proteins, their limitations have created a need for new ligands to large numbers of proteins. Here we propose a general concept to obtain protein affinity reagents that avoids animal immunization and iterative selection steps. Central to this process is the idea that small peptide libraries contain sequences that will bind to independent regions on a protein surface and that these ligands can be combined on synthetic scaffolds to create high affinity bivalent reagents. To demonstrate the feasibility of this approach, an array of 4000 unique 12-mer peptides was screened to identify sequences that bind to nonoverlapping sites on the yeast regulatory protein Gal80. Individual peptide ligands were screened at different distances using a novel DNA linking strategy to identify the optimal peptide pair and peptide pair separation distance required to transform two weaker ligands into a single high affinity protein capture reagent. A synthetic antibody or synbody was created with 5 nM affinity to Gal80 that functions in conventional ELISA and pull-down assays. We validated our synthetic antibody approach by creating a second synbody to human transferrin. In both cases, we observed an increase in binding affinity of approximately 1000-fold (DeltaDeltaG = approximately 4.1 kcal/mol) between the individual peptides and final bivalent synbody construct.
| S-EPMC2796340 | biostudies-literature