Algorithms for Protein Design

By Pablo Gainza, Hunter M. Nisonoff, Bruce Randall Donald. 2016

73 citations recorded by the source corpus.

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: (i) the input biophysical model, and (ii) 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.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. A. Panaitiu
2. Gevorg Grigoryan
3. Jianfu Zhou
4. Mostafa Karimi
5. Yang Shen
6. Shaowen Zhu
7. Yue Cao

In Molecular Biology

1. A. Panaitiu
2. Gevorg Grigoryan
3. Jianfu Zhou
4. Mostafa Karimi
5. Yang Shen
6. Shaowen Zhu
7. Yue Cao

Citations in the corpus

• De Novo Protein Design for Novel Folds using Guided Conditional Wasserstein Generative Adversarial Networks (gcWGAN) (2019, background): “…(especially deep residual networks) that learn from the sequence, structure, and co-evolution data (Wang et al. , 2017) The inverse problem of protein design is often similarly pursued following the energy minimum principle (Koga et al. , 2012; Gainza et al. , 2016;PierceandWinfree,2002).”
• A general-purpose protein design framework based on mining sequence–structure relationships in known protein structures (2018, background): “Several recent reviews provide excellent summaries of achievements and challenges in CPD, including by Donald and co-workers (22) (a thorough analysis of algorithmic aspects), by Ilan Samish and Regan et al.”
• Perturbing the energy landscape for improved packing during computational protein design (2020, unknown): “Most protein design programs use rotamer-based side chain optimization to identify amino acid sequences that will stabilize the desired protein structure or complex.(2) In these simulations, the side chains are modeled using a set of commonly observed discrete conformations, referred to as rotamers,”