Garcia Lopez, S. | Search bioRxiv

bioRxiv2024

Probabilistic Multiple Sequence Alignment using Spatial Transformations

AO_SCPLOWBSTRACTC_SCPLOWMultiple Sequence Alignment (MSA) has long been a prominent and critical tool in bioinformatics and computational biology. Its importance lies in its ability to provide valuable insights into the relationships between sequences and the evolutionary pressure leading to amino acid preferences at particular sites in a protein. Despite the recent advances in protein language models, MSAs remain critical in many applications, e.g. for state-of-the-art prediction of 3D structure and protein variant effects. Sequence alignment is typically considered a deterministic preprocessing step, leading to a single static MSA. Especially for low-similarity sequences, parts of an alignment will be subject to substantial uncertainty, which is disregarded when processing a static MSA. Earlier, HMM-based approaches handled this uncertainty by considering the full posterior ensemble over alignments. In this paper, we explore whether a similar approach is feasible within a modern deep learning approach, where we move beyond the Markovian restrictions of earlier models. In particular, we consider whether we can learn the alignment process as distribution over spatial transformations, in combination with a deep latent variable model of protein sequences. A proof-of-concept implementation of this work is available at https://github.com/deltadedirac/Explicit_Disentanglement_Molecules.

bioinformatics↗

bioRxiv2024

Cross-species vs species-specific models for protein melting temperature prediction

AO_SCPLOWBSTRACTC_SCPLOWProtein melting temperatures are important proxies for stability, and frequently probed in protein engineering campaigns, for instance for enzyme discovery and protein optimization. With the emergence of large datasets of melting temperatures for diverse natural proteins, it has become possible to train models to predict this quantity, and the literature has reported impressive performance values in terms of Spearman rho. The high correlation scores suggest that it should be possible to accurately predict melting temperature changes in engineered variants, and to reliably identify naturally thermostable proteins. However, in practice, results in these settings are often disappointing. In this paper, we explore this apparent discrepancy. We show that Spearman rho over cross-species data gives an overly optimistic impression of prediction performance, and that this metric reflects the ability to distinguish global differences in amino acid composition between species, rather than the specific effects of genetic variation. We proceed by investigating whether cross-species training on melting temperature is beneficial at all, compared to training specific models for each species. We address this question using four different transfer-learning approaches and a fine-tuning procedure. Surprisingly, we consistently find no benefit of cross-species training. We conclude that 1) current models for supervised prediction of melting temperature perform substantially worse than the literature suggests, and 2) that reliable transfer across species is still a challenging problem. An implementation of this work is available at https://github.com/deltadedirac/thermocontrast_tm

bioinformatics↗