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1. HFSP: high speed homology-driven function annotation of proteins

   https://doi.org/10.1093/bioinformatics/bty262  

   Mahlich, Yannick ; Steinegger, Martin ; Rost, Burkhard ; Bromberg, Yana ( June 2018 , Bioinformatics)

   The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins. The increase in the number of available sequences, however, has drastically increased the search space, thus significantly slowing down alignment methods.

   Here we describe homology-derived functional similarity of proteins (HFSP), a novel computational method that uses results of a high-speed alignment algorithm, MMseqs2, to infer functional similarity of proteins on the basis of their alignment length and sequence identity. We show that our method is accurate (85% precision) and fast (more than 40-fold speed increase over state-of-the-art). HFSP can help correct at least a 16% error in legacy curations, even for a resource of as high quality as Swiss-Prot. These findings suggest HFSP as an ideal resource for large-scale functional annotation efforts.

   Supplementary data are available at Bioinformatics online.

    

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2. GLIDER: function prediction from GLIDE-based neighborhoods

   https://doi.org/10.1093/bioinformatics/btac322  

   Devkota, Kapil ; Schmidt, Henri ; Werenski, Matt ; Murphy, James M. ; Erden, Mert ; Arsenescu, Victor ; Cowen, Lenore J. ; Valencia, ed., Alfonso ( May 2022 , Bioinformatics)

   Protein function prediction, based on the patterns of connection in a protein–protein interaction (or association) network, is perhaps the most studied of the classical, fundamental inference problems for biological networks. A highly successful set of recent approaches use random walk-based low-dimensional embeddings that tend to place functionally similar proteins into coherent spatial regions. However, these approaches lose valuable local graph structure from the network when considering only the embedding. We introduce GLIDER, a method that replaces a protein–protein interaction or association network with a new graph-based similarity network. GLIDER is based on a variant of our previous GLIDE method, which was designed to predict missing links in protein–protein association networks, capturing implicit local and global (i.e. embedding-based) graph properties.

   GLIDER outperforms competing methods on the task of predicting GO functional labels in cross-validation on a heterogeneous collection of four human protein–protein association networks derived from the 2016 DREAM Disease Module Identification Challenge, and also on three different protein–protein association networks built from the STRING database. We show that this is due to the strong functional enrichment that is present in the local GLIDER neighborhood in multiple different types of protein–protein association networks. Furthermore, we introduce the GLIDER graph neighborhood as a way for biologists to visualize the local neighborhood of a disease gene. As an application, we look at the local GLIDER neighborhoods of a set of known Parkinson’s Disease GWAS genes, rediscover many genes which have known involvement in Parkinson’s disease pathways, plus suggest some new genes to study.

   All code is publicly available and can be accessed here: https://github.com/kap-devkota/GLIDER.

   Supplementary data are available at Bioinformatics online.

    

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3. EvoEF2: accurate and fast energy function for computational protein design

   https://doi.org/10.1093/bioinformatics/btz740  

   Huang, Xiaoqiang ; Pearce, ,. Robin ; Zhang, Yang ; Elofsson, ed., Arne ( October 2019 , Bioinformatics)

   The accuracy and success rate of de novo protein design remain limited, mainly due to the parameter over-fitting of current energy functions and their inability to discriminate incorrect designs from correct designs.

   We developed an extended energy function, EvoEF2, for efficient de novo protein sequence design, based on a previously proposed physical energy function, EvoEF. Remarkably, EvoEF2 recovered 32.5%, 47.9% and 22.3% of all, core and surface residues for 148 test monomers, and was generally applicable to protein–protein interaction design, as it recapitulated 30.9%, 42.4%, 31.3% and 21.4% of all, core, interface and surface residues for 88 test dimers, significantly outperforming EvoEF on the native sequence recapitulation. We further used I-TASSER to evaluate the foldability of the 148 designed monomer sequences, where all of them were predicted to fold into structures with high fold- and atomic-level similarity to their corresponding native structures, as demonstrated by the fact that 87.8% of the predicted structures shared a root-mean-square-deviation less than 2 Å to their native counterparts. The study also demonstrated that the usefulness of physical energy functions is highly correlated with the parameter optimization processes, and EvoEF2, with parameters optimized using sequence recapitulation, is more suitable for computational protein sequence design than EvoEF, which was optimized on thermodynamic mutation data.

   The source code of EvoEF2 and the benchmark datasets are freely available at https://zhanglab.ccmb.med.umich.edu/EvoEF.

   Supplementary data are available at Bioinformatics online.

    

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4. Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function

   https://doi.org/10.1093/bioinformatics/btad208  

   Boadu, Frimpong ; Cao, Hongyuan ; Cheng, Jianlin ( June 2023 , Bioinformatics)

   Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap. Even though many methods have been developed to use protein sequences as input to predict function, much fewer methods leverage protein structures in protein function prediction because there was lack of accurate protein structures for most proteins until recently.

   We developed TransFun—a method using a transformer-based protein language model and 3D-equivariant graph neural networks to distill information from both protein sequences and structures to predict protein function. It extracts feature embeddings from protein sequences using a pre-trained protein language model (ESM) via transfer learning and combines them with 3D structures of proteins predicted by AlphaFold2 through equivariant graph neural networks. Benchmarked on the CAFA3 test dataset and a new test dataset, TransFun outperforms several state-of-the-art methods, indicating that the language model and 3D-equivariant graph neural networks are effective methods to leverage protein sequences and structures to improve protein function prediction. Combining TransFun predictions and sequence similarity-based predictions can further increase prediction accuracy.

   The source code of TransFun is available at https://github.com/jianlin-cheng/TransFun.

    

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5. DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites

   https://doi.org/10.1093/bioinformatics/btz721  

   Li, Fuyi ; Chen, Jinxiang ; Leier, André ; Marquez-Lago, Tatiana ; Liu, Quanzhong ; Wang, Yanze ; Revote, Jerico ; Smith, A. Ian ; Akutsu, Tatsuya ; Webb, Geoffrey I. ; et al ( September 2019 , Bioinformatics)

   Proteases are enzymes that cleave target substrate proteins by catalyzing the hydrolysis of peptide bonds between specific amino acids. While the functional proteolysis regulated by proteases plays a central role in the ‘life and death’ cellular processes, many of the corresponding substrates and their cleavage sites were not found yet. Availability of accurate predictors of the substrates and cleavage sites would facilitate understanding of proteases’ functions and physiological roles. Deep learning is a promising approach for the development of accurate predictors of substrate cleavage events.

   We propose DeepCleave, the first deep learning-based predictor of protease-specific substrates and cleavage sites. DeepCleave uses protein substrate sequence data as input and employs convolutional neural networks with transfer learning to train accurate predictive models. High predictive performance of our models stems from the use of high-quality cleavage site features extracted from the substrate sequences through the deep learning process, and the application of transfer learning, multiple kernels and attention layer in the design of the deep network. Empirical tests against several related state-of-the-art methods demonstrate that DeepCleave outperforms these methods in predicting caspase and matrix metalloprotease substrate-cleavage sites.

   The DeepCleave webserver and source code are freely available at http://deepcleave.erc.monash.edu/.

   Supplementary data are available at Bioinformatics online.

    

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