An all-atom protein generative model.

Proteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discrete nature of proteins encoded in the structure and sequence. We describe an all-atom diffusion model of protein structure, Protpardelle, which represents all sidechain states at once as a "superposition" state; superpositions defining a protein are collapsed into individual residue types and conformations during sample generation. When combined with sequence design methods, our model is able to codesign all-atom protein structure and sequence. Generated proteins are of good quality under the typical quality, diversity, and novelty metrics, and sidechains reproduce the chemical features and behavior of natural proteins. Finally, we explore the potential of our model to conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way.

Interleukin-2 superkines by computational design.

Affinity maturation of protein­protein interactions is an important approach in the development of therapeutic proteins such as cytokines. Typical experimental strategies involve targeting the cytokine-receptor interface with combinatorial libraries and then selecting for higher-affinity variants. Mutations to the binding scaffold are usually not considered main drivers for improved affinity. Here we demonstrate that computational design can provide affinity-enhanced variants of interleukin-2 (IL-2) "out of the box" without any requirement for interface engineering. Using a strategy of global IL-2 structural stabilization targeting metastable regions of the three-dimensional structure, rather than the receptor binding interfaces, we computationally designed thermostable IL-2 variants with up to 40-fold higher affinity for IL-2Rß without any library-based optimization. These IL-2 analogs exhibited CD25-independent activities on T and natural killer (NK) cells both in vitro and in vivo, mimicking the properties of the IL-2 superkine "super-2" that was engineered through yeast surface display [A. M. Levin et al., Nature, 484, 529­533 (2012)]. Structure-guided stabilization of cytokines is a powerful approach to affinity maturation with applications to many cytokine and protein­protein interactions.

Optical control of fast and processive engineered myosins in vitro and in living cells.

Precision tools for spatiotemporal control of cytoskeletal motor function are needed to dissect fundamental biological processes ranging from intracellular transport to cell migration and division. Direct optical control of motor speed and direction is one promising approach, but it remains a challenge to engineer controllable motors with desirable properties such as the speed and processivity required for transport applications in living cells. Here, we develop engineered myosin motors that combine large optical modulation depths with high velocities, and create processive myosin motors with optically controllable directionality. We characterize the performance of the motors using in vitro motility assays, single-molecule tracking and live-cell imaging. Bidirectional processive motors move efficiently toward the tips of cellular protrusions in the presence of blue light, and can transport molecular cargo in cells. Robust gearshifting myosins will further enable programmable transport in contexts ranging from in vitro active matter reconstitutions to microfabricated systems that harness molecular propulsion.

Sparks of function by de novo protein design.

Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.

An all-atom protein generative model.

Proteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discrete nature of proteins encoded in the structure and sequence. We describe an all-atom diffusion model of protein structure, Protpardelle, which instantiates a "superposition" over the possible sidechain states, and collapses it to conduct reverse diffusion for sample generation. When combined with sequence design methods, our model is able to co-design all-atom protein structure and sequence. Generated proteins are of good quality under the typical quality, diversity, and novelty metrics, and sidechains reproduce the chemical features and behavior of natural proteins. Finally, we explore the potential of our model conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way.

De Novo Design of a Highly Stable Ovoid TIM Barrel: Unlocking Pocket Shape towards Functional Design.

The ability to finely control the structure of protein folds is an important prerequisite to functional protein design. The TIM barrel fold is an important target for these efforts as it is highly enriched for diverse functions in nature. Although a TIM barrel protein has been designed de novo, the ability to finely alter the curvature of the central beta barrel and the overall architecture of the fold remains elusive, limiting its utility for functional design. Here, we report the de novo design of a TIM barrel with ovoid (twofold) symmetry, drawing inspiration from natural beta and TIM barrels with ovoid curvature. We use an autoregressive backbone sampling strategy to implement our hypothesis for elongated barrel curvature, followed by an iterative enrichment sequence design protocol to obtain sequences which yield a high proportion of successfully folding designs. Designed sequences are highly stable and fold to the designed barrel curvature as determined by a 2.1 Å resolution crystal structure. The designs show robustness to drastic mutations, retaining high melting temperatures even when multiple charged residues are buried in the hydrophobic core or when the hydrophobic core is ablated to alanine. As a scaffold with a greater capacity for hosting diverse hydrogen bonding networks and installation of binding pockets or active sites, the ovoid TIM barrel represents a major step towards the de novo design of functional TIM barrels.

Soybean Cyst Nematode Resistance Emerged via Artificial Selection of Duplicated Serine Hydroxymethyltransferase Genes.

A major soybean (Forrest cultivar) quantitative trait locus (QTL) gene, Rhg4, which controls resistance to soybean cyst nematodes (SCN), encodes the enzyme serine hydroxylmethyltransferase (SHMT). The resistant allele possesses two critical missense mutations (P130R and N358Y) compared to that of the sensitive allele, rhg4. To understand the evolutionary history of this gene, sequences of 117 SHMT family members from 18 representative plant species were used to reconstruct their phylogeny. According to this phylogeny, the plant SHMT gene family can be divided into two groups and four subgroups (Ia, Ib, IIa, and IIb). Belonging to the Subgroup Ia lineage, the rhg4 gene evolved from a recent duplication event in Glycine sp.. To further explore how the SCN-resistant allele emerged, both the rhg4 gene and its closest homolog, the rhg4h gene, were isolated from 33 cultivated and 68 wild soybean varieties. The results suggested that after gene duplication, the soybean rhg4 gene accumulated a higher number of non-synonymous mutations than rhg4h. Although a higher number of segregating sites and gene haplotypes were detected in wild soybeans than in cultivars, the SCN-resistant Rhg4 allele (represented by haplotype 4) was not found in wild varieties. Instead, a very similar allele, haplotype 3, was observed in wild soybeans at a frequency of 7.4%, although it lacked the two critical non-synonymous substitutions. Taken together, these findings support that the SCN-resistant Rhg4 allele likely emerged via artificial selection during the soybean domestication process, based on a SCN-sensitive allele inherited from wild soybeans.