Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins

Gandarilla-Pérez, Carlos A.; Pinilla, Sergio; Bitbol, Anne-Florence; Weigt, Martin

doi:10.1371/journal.pcbi.1011010

Cited by 4 publications

(27 citation statements)

References 49 publications

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“…It is important to note that phylogenetic correlations are nevertheless interesting and provide useful signal e.g. for the inference of protein partners among paralogs [47, 49, 50].…”

Section: Discussionmentioning

confidence: 99%

Impact of phylogeny on the inference of functional sectors from protein sequence data

Dietler,

Abbara,

Choudhury

et al. 2024

Preprint

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Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that natural selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information.

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“…It is important to note that phylogenetic correlations are nevertheless interesting and provide useful signal e.g. for the inference of protein partners among paralogs [47, 49, 50].…”

Section: Discussionmentioning

confidence: 99%

Impact of phylogeny on the inference of functional sectors from protein sequence data

Dietler,

Abbara,

Choudhury

et al. 2024

Preprint

Self Cite

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show abstract

“…The fact that DiffPALM outperforms existing coevolution methods [14,47,50] for shallow MSAs is reminiscent of the impressive performance of MSA Transformer at predicting structural contacts from shallow MSAs [51]. While traditional coevolution methods either compute local coevolution scores for two columns of an MSA [47] or build a global model for an MSA [14,15], MSA Transformer was trained on large ensembles of MSAs and shares parameters across them.…”

Section: Discussionmentioning

confidence: 99%

“…Explicitly combining coevolution and phylogeny using MSA Transformer is a promising direction to further improve partner pairing. Indeed, such an approach has already improved traditional coevolution methods [50]. Other ways of improving MSA usage by AFM have also been proposed [63] and could be combined with advances in pairing.…”

Section: Discussionmentioning

confidence: 99%

“…2 shows that the performance of DiffPALM significantly increases with the number of positive examples used, reaching almost perfect performance for the highest-confidence pairs (precision-10). [14], MI-IPA [47], and GA-IPA [50]. We also consider a pairing method based on the scores given by the ESM-2 (650M) single-sequence protein language model [5], see "Pairing based on a single-sequence language model".…”

Section: Diffpalm Outperforms Other Coevolution Methods On Small Msasmentioning

confidence: 99%

“…For the IPA method, we use 20 runs of MRA as starting point, and we add 5 fixed pairs at each new run, see "DiffPALM: Paralog matching based on MLM". The chance expectation, and the performance of DCA-IPA [14], MI-IPA [47], and GA-IPA [50] are reported as baselines. We also consider a pairing method based on the scores given by the ESM-2 (650M) protein language model [5], see "Pairing based on a single-sequence language model".…”

Section: S2 Supplementary Tablesmentioning

confidence: 99%

See 2 more Smart Citations

Pairing interacting protein sequences using masked language modeling

Lupo

Sgarbossa

Bitbol

2023

Preprint

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Predicting which proteins interact together from amino-acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments, such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called DiffPALM that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids. We show that it captures inter-chain coevolution, while it was trained on single-chain data, which means that it can be used out-of-distribution. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer, without significantly deteriorating any of those we tested. It also achieves competitive performance with using orthology-based pairing.

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Pairing interacting protein sequences using masked language modeling

Lupo,

Sgarbossa,

Bitbol

2024

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

Predicting which proteins interact together from amino acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments (MSAs), such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called Differentiable Pairing using Alignment-based Language Models (DiffPALM) that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids within protein chains. It also captures inter-chain coevolution, despite being trained on single-chain data. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. Starting from sequences paired by DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer. It also achieves competitive performance with using orthology-based pairing.

show abstract

Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins

Cited by 4 publications

References 49 publications

Impact of phylogeny on the inference of functional sectors from protein sequence data

Impact of phylogeny on the inference of functional sectors from protein sequence data

Pairing interacting protein sequences using masked language modeling

Pairing interacting protein sequences using masked language modeling

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