ReviewCurrent opinion in structural biology2016
Algorithms for protein design.
Review in Current opinion in structural biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- De novo functional protein sequence generation: overcoming data scarcity through regeneration and large language models.Briefings in bioinformatics · 2026Article
- Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti-Inflammatory and Gene Therapy Applications.Proteins · 2025Article
- Design of nanobody targeting SARS-CoV-2 spike glycoprotein using CDR-grafting assisted by molecular simulation and machine learning.PLoS computational biology · 2025Article
- Structure-based computational design of antibody mimetics: challenges and perspectives.FEBS open bio · 2025Review
- The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction.Biomolecules · 2024Review
- Exploring the Protein Sequence Space with Global Generative Models.Cold Spring Harbor perspectives in biology · 2023Review
- Redesigning methionyl-tRNA synthetase for β-methionine activity with adaptive landscape flattening and experiments.Protein science : a publication of the Protein Society · 2023Article
- From sequence to function through structure: Deep learning for protein design.Computational and structural biotechnology journal · 2023Review
- Resistor: An algorithm for predicting resistance mutations via Pareto optimization over multistate protein design and mutational signatures.Cell systems · 2022Article
- Protein design via deep learning.Briefings in bioinformatics · 2022Review
- Chiral evasion and stereospecific antifolate resistance in Staphylococcus aureus.PLoS computational biology · 2022Article
- Knowledge-Based Unfolded State Model for Protein Design.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Step-by-step design of proteins for small molecule interaction: A review on recent milestones.Protein science : a publication of the Protein Society · 2021Review
- Perturbing the energy landscape for improved packing during computational protein design.Proteins · 2021Article
- A Framework for Implementing Metaheuristic Algorithms Using Intercellular Communication.Frontiers in bioengineering and biotechnology · 2021Article
- Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network.PloS one · 2021Article
- De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks.Journal of chemical information and modeling · 2020Article
- Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface.PLoS computational biology · 2020Article
- Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering.International journal of molecular sciences · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Computational structure-based protein design programs are becoming an increasingly important tool in molecular biology. These programs compute protein sequences that are predicted to fold to a target structure and perform a desired function. The success of a program's predictions largely relies on two components: first, the input biophysical model, and second, the algorithm that computes the best sequence(s) and structure(s) according to the biophysical model. Improving both the model and the algorithm in tandem is essential to improving the success rate of current programs, and here we review recent developments in algorithms for protein design, emphasizing how novel algorithms enable the use of more accurate biophysical models. We conclude with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins and protein assemblies.
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Registered trials
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.