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1. In the twilight zone of protein sequence homology: do protein language models learn protein structure?

   https://doi.org/10.1093/bioadv/vbae119  

   Kabir, Anowarul ; Moldwin, Asher ; Bromberg, Yana ; Shehu, Amarda ; Gogovi, ed., Gideon ( August 2024 , Bioinformatics Advances)

   Protein language models based on the transformer architecture are increasingly improving performance on protein prediction tasks, including secondary structure, subcellular localization, and more. Despite being trained only on protein sequences, protein language models appear to implicitly learn protein structure. This paper investigates whether sequence representations learned by protein language models encode structural information and to what extent.

   We address this by evaluating protein language models on remote homology prediction, where identifying remote homologs from sequence information alone requires structural knowledge, especially in the “twilight zone” of very low sequence identity. Through rigorous testing at progressively lower sequence identities, we profile the performance of protein language models ranging from millions to billions of parameters in a zero-shot setting. Our findings indicate that while transformer-based protein language models outperform traditional sequence alignment methods, they still struggle in the twilight zone. This suggests that current protein language models have not sufficiently learned protein structure to address remote homology prediction when sequence signals are weak.

   We believe this opens the way for further research both on remote homology prediction and on the broader goal of learning sequence- and structure-rich representations of protein molecules. All code, data, and models are made publicly available.

    

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2. How to choose templates for modeling of protein complexes: Insights from benchmarking template‐based docking

   https://doi.org/10.1002/prot.25875  

   Chakravarty, Devlina ; McElfresh, GW ; Kundrotas, Petras J. ; Vakser, Ilya A. ( February 2020 , Proteins: Structure, Function, and Bioinformatics)

   Comparative docking is based on experimentally determined structures of protein‐protein complexes (templates), following the paradigm that proteins with similar sequences and/or structures form similar complexes. Modeling utilizing structure similarity of target monomers to template complexes significantly expands structural coverage of the interactome. Template‐based docking by structure alignment can be performed for the entire structures or by aligning targets to the bound interfaces of the experimentally determined complexes. Systematic benchmarking of docking protocols based on full and interface structure alignment showed that both protocols perform similarly, with top 1 docking success rate 26%. However, in terms of the models' quality, the interface‐based docking performed marginally better. The interface‐based docking is preferable when one would suspect a significant conformational change in the full protein structure upon binding, for example, a rearrangement of the domains in multidomain proteins. Importantly, if the same structure is selected as the top template by both full and interface alignment, the docking success rate increases 2‐fold for both top 1 and top 10 predictions. Matching structural annotations of the target and template proteins for template detection, as a computationally less expensive alternative to structural alignment, did not improve the docking performance. Sophisticated remote sequence homology detection added templates to the pool of those identified by structure‐based alignment, suggesting that for practical docking, the combination of the structure alignment protocols and the remote sequence homology detection may be useful in order to avoid potential flaws in generation of the structural templates library.

    

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3. Fast multiple sequence alignment via multi-armed bandits

   https://doi.org/10.1093/bioinformatics/btae225  

   Mazooji, Kayvon ; Shomorony, Ilan ( June 2024 , Bioinformatics)

   Multiple sequence alignment is an important problem in computational biology with applications that include phylogeny and the detection of remote homology between protein sequences. UPP is a popular software package that constructs accurate multiple sequence alignments for large datasets based on ensembles of hidden Markov models (HMMs). A computational bottleneck for this method is a sequence-to-HMM assignment step, which relies on the precise computation of probability scores on the HMMs. In this work, we show that we can speed up this assignment step significantly by replacing these HMM probability scores with alternative scores that can be efficiently estimated. Our proposed approach utilizes a multi-armed bandit algorithm to adaptively and efficiently compute estimates of these scores. This allows us to achieve similar alignment accuracy as UPP with a significant reduction in computation time, particularly for datasets with long sequences.

   The code used to produce the results in this paper is available on GitHub at: https://github.com/ilanshom/adaptiveMSA.

    

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4. Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading

   https://doi.org/10.3389/fmolb.2021.643752  

   Bhattacharya, Sutanu ; Roche, Rahmatullah ; Shuvo, Md Hossain ; Bhattacharya, Debswapna ( May 2021 , Frontiers in Molecular Biosciences)

   null (Ed.)

   Sequence-based protein homology detection has emerged as one of the most sensitive and accurate approaches to protein structure prediction. Despite the success, homology detection remains very challenging for weakly homologous proteins with divergent evolutionary profile. Very recently, deep neural network architectures have shown promising progress in mining the coevolutionary signal encoded in multiple sequence alignments, leading to reasonably accurate estimation of inter-residue interaction maps, which serve as a rich source of additional information for improved homology detection. Here, we summarize the latest developments in protein homology detection driven by inter-residue interaction map threading. We highlight the emerging trends in distant-homology protein threading through the alignment of predicted interaction maps at various granularities ranging from binary contact maps to finer-grained distance and orientation maps as well as their combination. We also discuss some of the current limitations and possible future avenues to further enhance the sensitivity of protein homology detection. 

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5. Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN

   https://doi.org/10.1038/s41592-024-02191-z  

   Rosen, Yanay ; Brbić, Maria ; Roohani, Yusuf ; Swanson, Kyle ; Li, Ziang ; Leskovec, Jure ( February 2024 , Nature Methods)

   Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, interspecies genomic differences limit the joint analysis of cross-species datasets to homologous genes. Here we present SATURN, a deep learning method for learning universal cell embeddings that encodes genes’ biological properties using protein language models. By coupling protein embeddings from language models with RNA expression, SATURN integrates datasets profiled from different species regardless of their genomic similarity. SATURN can detect functionally related genes coexpressed across species, redefining differential expression for cross-species analysis. Applying SATURN to three species whole-organism atlases and frog and zebrafish embryogenesis datasets, we show that SATURN can effectively transfer annotations across species, even when they are evolutionarily remote. We also demonstrate that SATURN can be used to find potentially divergent gene functions between glaucoma-associated genes in humans and four other species.

    

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