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1. eCOMPASS: evaluative comparison of multiple protein alignments by statistical score

   https://doi.org/10.1093/bioinformatics/btab374  

   Neuwald, Andrew F; Kolaczkowski, Bryan D; Altschul, Stephen F (May 2021, Bioinformatics)

   Ponty, Yann (Ed.)

   Abstract Motivation Detecting subtle biologically relevant patterns in protein sequences often requires the construction of a large and accurate multiple sequence alignment (MSA). Methods for constructing MSAs are usually evaluated using benchmark alignments, which, however, typically contain very few sequences and are therefore inappropriate when dealing with large numbers of proteins. Results eCOMPASS addresses this problem using a statistical measure of relative alignment quality based on direct coupling analysis (DCA): To maintain protein structural integrity over evolutionary time, substitutions at one residue position typically result in compensating substitutions at other positions. eCOMPASS computes the statistical significance of the congruence between high scoring directly coupled pairs and 3D contacts in corresponding structures, which depends upon properly aligned homologous residues. We illustrate eCOMPASS using both simulated and real MSAs. Availability and Implementation The eCOMPASS executable, C ++ open source code and input data sets are available at https://www.igs.umaryland.edu/labs/neuwald/software/compass. Supplementary information Supplementary data are available at Bioinformatics online. 

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2. End-to-end learning of multiple sequence alignments with differentiable Smith–Waterman

   https://doi.org/10.1093/bioinformatics/btac724  

   Petti, Samantha; Bhattacharya, Nicholas; Rao, Roshan; Dauparas, Justas; Thomas, Neil; Zhou, Juannan; Rush, Alexander M.; Koo, Peter; Ovchinnikov, Sergey; Borgwardt, ed., Karsten (November 2022, Bioinformatics)

   Abstract MotivationMultiple sequence alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-processing step, without any guidance from the application it will be used for. ResultsHere, we implement a smooth and differentiable version of the Smith–Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion. To demonstrate its utility, we introduce SMURF (Smooth Markov Unaligned Random Field), a new method that jointly learns an alignment and the parameters of a Markov Random Field for unsupervised contact prediction. We find that SMURF learns MSAs that mildly improve contact prediction on a diverse set of protein and RNA families. As a proof of concept, we demonstrate that by connecting our differentiable alignment module to AlphaFold2 and maximizing predicted confidence, we can learn MSAs that improve structure predictions over the initial MSAs. Interestingly, the alignments that improve AlphaFold predictions are self-inconsistent and can be viewed as adversarial. This work highlights the potential of differentiable dynamic programming to improve neural network pipelines that rely on an alignment and the potential dangers of optimizing predictions of protein sequences with methods that are not fully understood. Availability and implementationOur code and examples are available at: https://github.com/spetti/SMURF. Supplementary informationSupplementary data are available at Bioinformatics online. 

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3. Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data

   https://doi.org/10.1038/s41592-023-02130-4  

   Zheng, Wei; Wuyun, Qiqige; Li, Yang; Zhang, Chengxin; Freddolino, Lydia; Zhang, Yang (January 2024, Nature Methods)

   Abstract Leveraging iterative alignment search through genomic and metagenome sequence databases, we report the DeepMSA2 pipeline for uniform protein single- and multichain multiple-sequence alignment (MSA) construction. Large-scale benchmarks show that DeepMSA2 MSAs can remarkably increase the accuracy of protein tertiary and quaternary structure predictions compared with current state-of-the-art methods. An integrated pipeline with DeepMSA2 participated in the most recent CASP15 experiment and created complex structural models with considerably higher quality than the AlphaFold2-Multimer server (v.2.2.0). Detailed data analyses show that the major advantage of DeepMSA2 lies in its balanced alignment search and effective model selection, and in the power of integrating huge metagenomics databases. These results demonstrate a new avenue to improve deep learning protein structure prediction through advanced MSA construction and provide additional evidence that optimization of input information to deep learning-based structure prediction methods must be considered with as much care as the design of the predictor itself. 

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4. ViralMSA: massively scalable reference-guided multiple sequence alignment of viral genomes

   https://doi.org/10.1093/bioinformatics/btaa743  

   Moshiri, Niema (August 2020, Bioinformatics)

   Robinson, Peter (Ed.)

   Abstract Motivation In molecular epidemiology, the identification of clusters of transmissions typically requires the alignment of viral genomic sequence data. However, existing methods of multiple sequence alignment (MSA) scale poorly with respect to the number of sequences. Results ViralMSA is a user-friendly reference-guided MSA tool that leverages the algorithmic techniques of read mappers to enable the MSA of ultra-large viral genome datasets. It scales linearly with the number of sequences, and it is able to align tens of thousands of full viral genomes in seconds. However, alignments produced by ViralMSA omit insertions with respect to the reference genome. Availability and implementation ViralMSA is freely available at https://github.com/niemasd/ViralMSA as an open-source software project. Contact niema@ucsd.edu Supplementary information Supplementary data are available at Bioinformatics online. 

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5. Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks

   https://doi.org/10.1371/journal.pcbi.1008865  

   Li, Yang; Zhang, Chengxin; Bell, Eric W.; Zheng, Wei; Zhou, Xiaogen; Yu, Dong-Jun; Zhang, Yang (March 2021, PLOS Computational Biology)

   Kolodny, Rachel (Ed.)

   The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top- L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top- L /5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library. 

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