Evidence map›Paper›PMID 28191922›Full record

ReviewAccounts of chemical research2017

Synergistic Integration of Experimental and Simulation Approaches for the de Novo Design of Silk-Based Materials.

Wenwen Huang, Davoud Ebrahimi, Nina Dinjaski, Anna Tarakanova, Markus J Buehler, Joyce Y Wong, David L Kaplan

Open access · greenAbstract readReview
In one paragraph

Review in Accounts of chemical research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
3.0field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 63 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Advanced silk materials for musculoskeletal tissue regeneration.Frontiers in bioengineering and biotechnology · 2023
    Review
  8. Review
  9. Gelation Methods to Assemble Fibrous Proteins.Methods in molecular biology (Clifton, N.J.) · 2021
    Article
  10. Stimuli-responsive composite biopolymer actuators with selective spatial deformation behavior.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  11. Review
  12. Article
  13. Nanofibrils in nature and materials engineering.Nature reviews. Materials · 2018
    Article
  14. Review
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Wenwen HuangDepartment of Biomedical Engineering, Tufts University , 4 Colby Street, Medford, Massachusetts 02155, United States.
Davoud EbrahimiLaboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology , Cambridge, Massachusetts 02139, United States.
Nina DinjaskiDepartment of Biomedical Engineering, Tufts University , 4 Colby Street, Medford, Massachusetts 02155, United States.
Anna TarakanovaLaboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology , Cambridge, Massachusetts 02139, United States.
Markus J BuehlerLaboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology , Cambridge, Massachusetts 02139, United States.ORCID 0000-0002-4173-9659
Joyce Y WongDepartment of Biomedical Engineering, Boston University , 44 Cummington Street, Boston, Massachusetts 02215, United States.
David L KaplanDepartment of Biomedical Engineering, Tufts University , 4 Colby Street, Medford, Massachusetts 02155, United States.ORCID 0000-0002-9245-7774
Massachusetts Institute of Technology · USTufts University · USBoston University · US

Funding

Translational Bioreactors - Engineering Complex TissuesP41EB002520 · NIBIB · TUFTS UNIVERSITY MEDFORD · PI KAPLAN, DAVID L. · 2004 to 2018
$16.7M
Models to Predict Protein Biomaterial PerformanceU01EB014976 · NIBIB · TUFTS UNIVERSITY MEDFORD · PI BUEHLER, MARKUS J., KAPLAN, DAVID L. · 2012 to 2020
$5.2M
Cross-scale interactions between mineral and collagen for tendon-bone attachmentU01EB016422 · NIBIB · WASHINGTON UNIVERSITY · PI GENIN, GUY M, THOMOPOULOS, STAVROS · 2013 to 2017
$2.4M
NIBIB NIH HHS P41 EB002520NIBIB NIH HHS U01 EB014976
6 · The paper itself

Abstract

Tailored biomaterials with tunable functional properties are crucial for a variety of task-specific applications ranging from healthcare to sustainable, novel bio-nanodevices. To generate polymeric materials with predictive functional outcomes, exploiting designs from nature while morphing them toward non-natural systems offers an important strategy. Silks are Nature's building blocks and are produced by arthropods for a variety of uses that are essential for their survival. Due to the genetic control of encoded protein sequence, mechanical properties, biocompatibility, and biodegradability, silk proteins have been selected as prototype models to emulate for the tunable designs of biomaterial systems. The bottom up strategy of material design opens important opportunities to create predictive functional outcomes, following the exquisite polymeric templates inspired by silks. Recombinant DNA technology provides a systematic approach to recapitulate, vary, and evaluate the core structure peptide motifs in silks and then biosynthesize silk-based polymers by design. Post-biosynthesis processing allows for another dimension of material design by controlled or assisted assembly. Multiscale modeling, from the theoretical prospective, provides strategies to explore interactions at different length scales, leading to selective material properties. Synergy among experimental and modeling approaches can provide new and more rapid insights into the most appropriate structure-function relationships to pursue while also furthering our understanding in terms of the range of silk-based systems that can be generated. This approach utilizes nature as a blueprint for initial polymer designs with useful functions (e.g., silk fibers) but also employs modeling-guided experiments to expand the initial polymer designs into new domains of functional materials that do not exist in nature. The overall path to these new functional outcomes is greatly accelerated via the integration of modeling with experiment. In this Account, we summarize recent advances in understanding and functionalization of silk-based protein systems, with a focus on the integration of simulation and experiment for biopolymer design. Spider silk was selected as an exemplary protein to address the fundamental challenges in polymer designs, including specific insights into the role of molecular weight, hydrophobic/hydrophilic partitioning, and shear stress for silk fiber formation. To expand current silk designs toward biointerfaces and stimuli responsive materials, peptide modules from other natural proteins were added to silk designs to introduce new functions, exploiting the modular nature of silk proteins and fibrous proteins in general. The integrated approaches explored suggest that protein folding, silk volume fraction, and protein amino acid sequence changes (e.g., mutations) are critical factors for functional biomaterial designs. In summary, the integrated modeling-experimental approach described in this Account suggests a more rationally directed and more rapid method for the design of polymeric materials. It is expected that this combined use of experimental and computational approaches has a broad applicability not only for silk-based systems, but also for other polymer and composite materials.

Indexed as

AnimalsBiocompatible MaterialsSilkBiocompatible MaterialsSilk

Identifiers

PMID28191922
PMCPMC9310429
OpenAlexW2588631361

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.