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Abstract Domains are functional and structural units of proteins that govern various biological functions performed by the proteins. Therefore, the characterization of domains in a protein can serve as a proper functional representation of proteins. Here, we employ a self-supervised protocol to derive functionally consistent representations for domains by learning domain-Gene Ontology (GO) co-occurrences and associations. The domain embeddings we constructed turned out to be effective in performing actual function prediction tasks. Extensive evaluations showed that protein representations using the domain embeddings are superior to those of large-scale protein language models in GO prediction tasks. Moreover, the new function prediction method built on the domain embeddings, named Domain-PFP, substantially outperformed the state-of-the-art function predictors. Additionally, Domain-PFP demonstrated competitive performance in the CAFA3 evaluation, achieving overall the best performance among the top teams that participated in the assessment. 

Abstract Signals of natural selection can be quickly eroded in high gene flow systems, curtailing efforts to understand how and when genetic adaptation occurs in the ocean. This long‐standing, unresolved topic in ecology and evolution has renewed importance because changing environmental conditions are driving range expansions that may necessitate rapid evolutionary responses. One example occurs in Kellet's whelk (Kelletia kelletii), a common subtidal gastropod with an ~40‐ to 60‐day pelagic larval duration that expanded their biogeographic range northwards in the 1970s by over 300 km. To test for genetic adaptation, we performed a series of experimental crosses with Kellet's whelk adults collected from their historical (HxH) and recently expanded range (ExE), and conducted RNA‐Seq on offspring that we reared in a common garden environment. We identified 2770 differentially expressed genes (DEGs) between 54 offspring samples with either only historical range (HxH offspring) or expanded range (ExE offspring) ancestry. Using SNPs called directly from the DEGs, we assigned samples of known origin back to their range of origin with unprecedented accuracy for a marine species (92.6% and 94.5% for HxH and ExE offspring, respectively). The SNP with the highest predictive importance occurred on triosephosphate isomerase (TPI), an essential metabolic enzyme involved in cold stress response.TPIwas significantly upregulated and contained a non‐synonymous mutation in the expanded range. Our findings pave the way for accurately identifying patterns of dispersal, gene flow and population connectivity in the ocean by demonstrating that experimental transcriptomics can reveal mechanisms for how marine organisms respond to changing environmental conditions. 

Computational function prediction is one of the most important problems in bioinformatics as elucidating the function of genes is a central task in molecular biology and genomics. Most of the existing function prediction methods use protein sequences as the primary source of input information because the sequence is the most available information for query proteins. There are attempts to consider other attributes of query proteins. Among these attributes, the three-dimensional (3D) structure of proteins is known to be very useful in identifying the evolutionary relationship of proteins, from which functional similarity can be inferred. Here, we report a novel protein function prediction method, ContactPFP, which uses predicted residue-residue contact maps as input structural features of query proteins. Although 3D structure information is known to be useful, it has not been routinely used in function prediction because the 3D structure is not experimentally determined for many proteins. In ContactPFP, we overcome this limitation by using residue-residue contact prediction, which has become increasingly accurate due to rapid development in the protein structure prediction field. ContactPFP takes a query protein sequence as input and uses predicted residue-residue contact as a proxy for the 3D protein structure. To characterize how predicted contacts contribute to function prediction accuracy, we compared the performance of ContactPFP with several well-established sequence-based function prediction methods. The comparative study revealed the advantages and weaknesses of ContactPFP compared to contemporary sequence-based methods. There were many cases where it showed higher prediction accuracy. We examined factors that affected the accuracy of ContactPFP using several illustrative cases that highlight the strength of our method. 

Abstract Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA’s feature at the inter-residue level, we added an attention layer to the deep neural network. We show that combining four MSAs of different E-value cutoffs improved the model prediction performance as compared to single E-value MSA features. A further improvement was observed when an attention layer was used and even more when additional prediction tasks of bond angle predictions were added. The improvement of distance predictions were successfully transferred to achieve better protein tertiary structure modeling. 

Abstract Motivation Protein structure prediction remains as one of the most important problems in computational biology and biophysics. In the past few years, protein residue–residue contact prediction has undergone substantial improvement, which has made it a critical driving force for successful protein structure prediction. Boosting the accuracy of contact predictions has, therefore, become the forefront of protein structure prediction. Results We show a novel contact map refinement method, ContactGAN, which uses Generative Adversarial Networks (GAN). ContactGAN was able to make a significant improvement over predictions made by recent contact prediction methods when tested on three datasets including protein structure modeling targets in CASP13 and CASP14. We show improvement of precision in contact prediction, which translated into improvement in the accuracy of protein tertiary structure models. On the other hand, observed improvement over trRosetta was relatively small, reasons for which are discussed. ContactGAN will be a valuable addition in the structure prediction pipeline to achieve an extra gain in contact prediction accuracy. Availability and implementation https://github.com/kiharalab/ContactGAN. Supplementary information Supplementary data are available at Bioinformatics online. 

ABSTRACT Biomolecules rely on water and ions for stable folding, but these interactions are often transient, dynamic, or disordered and thus hidden from experiments and evaluation challenges that represent biomolecules as single, ordered structures. Here, we compare blindly predicted ensembles of water and ion structure to the cryo‐EM densities observed around theTetrahymenaribozyme at 2.2–2.3 Å resolution, collected through target R1260 in the CASP16 competition. Twenty‐six groups participated in this solvation “cryo‐ensemble” prediction challenge, submitting over 350 million atoms in total, offering the first opportunity to compare blind predictions of dynamic solvent shell ensembles to cryo‐EM density. Predicted atomic ensembles were converted to density through local alignment and these densities were compared to the cryo‐EM densities using Pearson correlation, Spearman correlation, mutual information, and precision‐recall curves. These predictions show that an ensemble representation is able to capture information of transient or dynamic water and ions better than traditional atomic models, but there remains a large accuracy gap to the performance ceiling set by experimental uncertainty. Overall, molecular dynamics approaches best matched the cryo‐EM density, with blind predictions from bussilab_plain_md, SoutheRNA, bussilab_replex, coogs2, and coogs3 outperforming the baseline molecular dynamics prediction. This study indicates that simulations of water and ions can be quantitatively evaluated with cryo‐EM maps. We propose that further community‐wide blind challenges can drive and evaluate progress in modeling water, ions, and other previously hidden components of biomolecular systems.