Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14.

The trRosetta structure prediction method employs deep learning to generate predicted residue-residue distance and orientation distributions from which 3D models are built. We sought to improve the method by incorporating as inputs (in addition to sequence information) both language model embeddings and template information weighted by sequence similarity to the target. We also developed a refinement pipeline that recombines models generated by template-free and template utilizing versions of trRosetta guided by the DeepAccNet accuracy predictor. Both benchmark tests and CASP results show that the new pipeline is a considerable improvement over the original trRosetta, and it is faster and requires less computing resources, completing the entire modeling process in a median < 3 h in CASP14. Our human group improved results with this pipeline primarily by identifying additional homologous sequences for input into the network. We also used the DeepAccNet accuracy predictor to guide Rosetta high-resolution refinement for submissions in the regular and refinement categories; although performance was quite good on a CASP relative scale, the overall improvements were rather modest in part due to missing inter-domain or inter-chain contacts.

Improved protein structure refinement guided by deep learning based accuracy estimation.

We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution, and the network should be broadly useful for assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error. Incorporation of the accuracy predictions at multiple stages in the Rosetta refinement protocol considerably increased the accuracy of the resulting protein structure models, illustrating how deep learning can improve search for global energy minima of biomolecules.

AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification.

ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a 'control' dataset to remove background signals from a immunoprecipitation (IP) 'target' dataset. We introduce the AIControl framework, which eliminates the need to obtain a control dataset and instead identifies binding peaks by estimating the distributions of background signals from many publicly available control ChIP-seq datasets. We thereby avoid the cost of running control experiments while simultaneously increasing the accuracy of binding location identification. Specifically, AIControl can (i) estimate background signals at fine resolution, (ii) systematically weigh the most appropriate control datasets in a data-driven way, (iii) capture sources of potential biases that may be missed by one control dataset and (iv) remove the need for costly and time-consuming control experiments. We applied AIControl to 410 IP datasets in the ENCODE ChIP-seq database, using 440 control datasets from 107 cell types to impute background signal. Without using matched control datasets, AIControl identified peaks that were more enriched for putative binding sites than those identified by other popular peak callers that used a matched control dataset. We also demonstrated that our framework identifies binding sites that recover documented protein interactions more accurately.

Sexual ancestors generated an obligate asexual and globally dispersed clone within the model diatom species Thalassiosira pseudonana.

Sexual reproduction roots the eukaryotic tree of life, although its loss occurs across diverse taxa. Asexual reproduction and clonal lineages persist in these taxa despite theoretical arguments suggesting that individual clones should be evolutionarily short-lived due to limited phenotypic diversity. Here, we present quantitative evidence that an obligate asexual lineage emerged from a sexual population of the marine diatom Thalassiosira pseudonana and rapidly expanded throughout the world's oceans. Whole genome comparisons identified two lineages with characteristics expected of sexually reproducing strains in Hardy-Weinberg equilibrium. A third lineage displays genomic signatures for the functional loss of sexual reproduction followed by a recent global colonization by a single ancestral genotype. Extant members of this lineage are genetically differentiated and phenotypically plastic, potentially allowing for rapid adaptation when they are challenged by natural selection. Such mechanisms may be expected to generate new clones within marginal populations of additional unicellular species, facilitating the exploration and colonization of novel environments, aided by exponential growth and ease of dispersal.