Dataset Information

Protein design automation.

ABSTRACT: We have conceived and implemented a cyclical protein design strategy that couples theory, computation, and experimental testing. The combinatorially large number of possible sequences and the incomplete understanding of the factors that control protein structure are the primary obstacles in protein design. Our protein design automation algorithm objectively predicts protein sequences likely to achieve a desired fold. Using a rotamer description of the side chains, we implemented a fast discrete search algorithm based on the Dead-End Elimination Theorem to rapidly find the globally optimal sequence in its optimal geometry from the vast number of possible solutions. Rotamer sequences were scored for steric complementarity using a van der Waals potential. A Monte Carlo search was then executed, starting at the optimal sequence, in order to find other high-scoring sequences. As a test of the design methodology, high-scoring sequences were found for the buried hydrophobic residues of a homodimeric coiled coil based on GCN4-p1. The corresponding peptides were synthesized and characterized by CD spectroscopy and size-exclusion chromatography. All peptides were dimeric and nearly 100% helical at 1 degree C, with melting temperatures ranging from 24 degrees C to 57 degrees C. A quantitative structure activity relation analysis was performed on the designed peptides, and a significant correlation was found with surface area burial. Incorporation of a buried surface area potential in the scoring of sequences greatly improved the correlation between predicted and measured stabilities and demonstrated experimental feedback in a complete design cycle.

SUBMITTER: Dahiyat BI

PROVIDER: S-EPMC2143401 | biostudies-other | 1996 May

REPOSITORIES: biostudies-other

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Protein design automation.

Dahiyat B I BI Mayo S L SL

Protein science : a publication of the Protein Society 19960501 5

We have conceived and implemented a cyclical protein design strategy that couples theory, computation, and experimental testing. The combinatorially large number of possible sequences and the incomplete understanding of the factors that control protein structure are the primary obstacles in protein design. Our protein design automation algorithm objectively predicts protein sequences likely to achieve a desired fold. Using a rotamer description of the side chains, we implemented a fast discrete ...[more]

PMID: 8732761

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Project description:INTRODUCTION:Haemorrhagic stroke is of significant healthcare concern due to its association with high mortality and lasting impact on the survivors' quality of life. Treatment decisions and clinical outcomes depend strongly on the size, spread and location of the haematoma. Non-contrast CT (NCCT) is the primary neuroimaging modality for haematoma assessment in haemorrhagic stroke diagnosis. Current procedures do not allow convenient NCCT-based haemorrhage volume calculation in clinical settings, while research-based approaches are yet to be tested for clinical utility; there is a demonstrated need for developing effective solutions. The project under review investigates the development of an automatic NCCT-based haematoma computation tool in support of accurate quantification of haematoma volumes. METHODS AND ANALYSIS:Several existing research methods for haematoma volume estimation are studied. Selected methods are tested using NCCT images of patients diagnosed with acute haemorrhagic stroke. For inter-rater and intrarater reliability evaluation, different raters will analyse haemorrhage volumes independently. The efficiency with respect to time of haematoma volume assessments will be examined to compare with the results from routine clinical evaluations and planimetry assessment that are known to be more accurate. The project will target the development of an enhanced solution by adapting existing methods and integrating machine learning algorithms. NCCT-based information of brain haemorrhage (eg, size, volume, location) and other relevant information (eg, age, sex, risk factor, comorbidities) will be used in relation to clinical outcomes with future project development. Validity and reliability of the solution will be examined for potential clinical utility. ETHICS AND DISSEMINATION:The project including procedures for deidentification of NCCT data has been ethically approved. The study involves secondary use of existing data and does not require new consent of participation. The team consists of clinical neuroimaging scientists, computing scientists and clinical professionals in neurology and neuroradiology and includes patient representatives. Research outputs will be disseminated following knowledge translation plans towards improving stroke patient care. Significant findings will be published in scientific journals. Anticipated deliverables include computer solutions for improved clinical assessment of haematoma using NCCT.

Dataset Information

Protein design automation.

Publications

Protein design automation.

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