From sequence to function through structure: deep learning for protein design

Abstract

The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve conditional and controllable protein design, researchers at the interface of artificial intelligence and biology leverage advances in natural language processing (NLP) and computer vision techniques, coupled with advances in computing hardware to learn patterns from growing biological databases, curated annotations thereof, or both. Once learned, these patterns can be leveraged to provide novel insights into mechanistic biology and the design of biomolecules. However, navigating and understanding the practical applications for the many recent protein design tools is complex. To facilitate this, we 1) document recent advances in deep learning (DL) assisted protein design from the last three years, 2) present a practical pipeline that allows to go from de novo-generated sequences to their predicted properties and web-powered visualization within minutes, and 3) leverage it to suggest a generated protein sequence which might be used to engineer a biosynthetic gene cluster to produce a molecular glue-like compound. Lastly, we discuss challenges and highlight opportunities for the protein design field.

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics

“…They have also been employed to understand the language of nucleic acids (DNA/RNA) and proteins. Protein language models (pLMs), using amino acid alphabets, are the most heavily investigated biological LLMs Rives et al (2021); Elnaggar et al (2022), with demonstrated success in downstream tasks such as protein function prediction Unsal et al (2022) and engineering Ferruz et al (2022). Nucleotide LLMs, using DNA/RNA alphabets, are still understudied Avsec et al (2021).…”

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics

“…They have also been employed to understand the language of nucleic acids (DNA/RNA) and proteins. Protein language models (PLMs), using amino acid alphabets, are the most heavily investigated biological LLMs (Elnaggar et al, 2022, Rives et al, 2021, with demonstrated success in many downstream tasks (e.g., protein function prediction (Unsal et al, 2022)) and engineering (Ferruz et al, 2022). Nucleotide LLMs, using DNA/RNA alphabets, are still understudied (Avsec et al, 2021).…”

Cytochrome P450 Enzyme Design by Constraining Catalytic Pocket in Diffusion model

“…The rapid development of deep learning has opened up a new method to acquire novel P450s with desired characteristics. Even though impressive achievements have been witnessed in protein structure prediction 17, 18 , the desired functional design still is a big challenge 19, 20 . Recent developments in protein design leveraged by deep learning methods encompass a broad spectrum.…”