Long-read RNA sequencing technologies are establishing themselves as the primary techniques to detect novel isoforms, and many such analyses are dependent on read alignments. However, the error rate and sequencing length of the reads create new challenges for accurately aligning them, particularly around small exons.We present an alignment method uLTRA for long RNA sequencing reads based on a novel two-pass collinear chaining algorithm. We show that uLTRA produces higher accuracy over state-of-the-art aligners with substantially higher accuracy for small exons on simulated and synthetic data. On simulated data, uLTRA achieves an accuracy of about 60% for exons of length 10 nucleotides or smaller and close to 90% accuracy for exons of length between 11 and 20 nucleotides. On biological data where true read location is unknown, we show several examples where uLTRA aligns to known and novel isoforms containing small exons that are not detected with other aligners. While uLTRA obtains its accuracy using annotations, it can also be used as a wrapper around minimap2 to align reads outside annotated regions.https://github.com/ksahlin/ultra.uLTRA is available at Bioinformatics online. The transcriptome has been identified as an important link between DNA and phenotype and is therefore analyzed in various biological and biomedical studies. For these analyses, RNA sequencing has established itself as the primary experimental method. Some of the most common transcriptome analyses using RNA sequencing data include predicting and detecting isoforms and quantifying their abundance in the sample. These analyses are fundamentally underpinned by the alignment of reads to genomes. As a transcriptomic read can contain multiple exons, alignment algorithms are required to handle split alignment of a read to multiple exonic regions of the genome, referred to as a spliced alignment.Spliced alignment is a challenging computational problem, and a plethora of different alignment algorithms have been proposed for splice alignment of short-read RNA-seq, with some of the key algorithmic advances given in TopHat (Long-read transcriptome sequencing protocols such as Pacific Biosciences (PacBio) Iso-Seq sequencing . Firstly, because of their length, small exons can be highly repetitive in the genome and be shorter than the required seed match length of the aligner. Secondly, even if the size is larger than the minimum seed match length, a small exon is less likely to contain seed matches if there are errors present. The inability to align a read to small exons may cause downstream analysis tools to predict and quantify erroneous isoforms. In addition, we show in this study that splice aligners that use junction-specific alignment penalties can create spurious junctions by overfitting alignments to canonical splice sites such as GT-AG junctions.To alleviate these limitations, we have designed and implemented a splice alignment algorithm uLTRA that aligns long-reads to a genome using an exon annotation. uLTRA uses a novel two-pass collinear chaining algorithm. In the first pass, uLTRA, similarly to https://github.com/ksahlin/ultra.We demonstrate using simulated datasets that uLTRA, both as a stand-alone aligner and as a wrapper around minimap2, produces much more accurate alignments than other aligners, particularly for small exons. We also use a dataset with ONT sequencing of synthetic SIRV transcripts (known isoforms) to demonstrate that uLTRA aligns more reads to transcripts that are known to be in the sample. Furthermore, we show on biological datasets from both PacBio and ONT that uLTRA aligns more reads to annotated isoforms and has alignments to more distinct isoform structures. Finally, we demonstrate that uLTRA produces alignments to known and novel isoform structures in the PacBio Alzheimer dataset that are not found by other aligners. These isoform structures come from genes that have been studied or linked to Alzheimer’s disease and motivate the utility of our method for a range of downstream analysis tasks such as isoform prediction and detection, splice-site analysis, isoform quantification and more. uLTRA is available at uLTRA solves the algorithmic problem of chaining with overlaps to find alignments. The method consists of three steps. An overview of uLTRA is shown in To align reads, uLTRA first finds maximal exact matches (MEMs) between the reads and the parts and flanks using slaMEM  Fig. 1BFig. 1B. In the third step, we design a dynamic programming formulation that aims to chain together the combination of genomic segments that best fits the read. Each solution to the MEM chaining is processed as follows. The MEMs in the chaining solution overlap distinct segments on the genome . If A part is defined as the smallest genomic region fully covering a set of overlapping exons . By consS is a collinear subset of A, meaning that Coverage(S) is defined as the number of identities in an alignment induced by S, i.e. the length of the anchor-restricted LCS (longest common subsequence) of reference and the read, where anchor now means a MEM  is the overall length of MEMs in S, but if there are, the score is adjusted by adding only the minimum length of the non-overlapping parts of the consecutive MEM intervals (S that maximizes Coverage(S), we use Algorithm 1 in A Maximal Exact Match (MEM) ecedence ]. Coverans a MEM : If therntervals . Here, wi. Let A under the weak precedence constraint  of a MAM-chain is defined asWe refer to an approximate match, as an alignment of a genome segment ng edlib . The lennstraint . For twoWe find the chain X nucleotides  out of the total aligned nucleotides is considered genomic and not realigned with uLTRA. We use an interval-tree data structure to hold the indexed regions to find overlap of a read and indexed regions. This permits an Q is the number of intervals on the chromosome to which the read is aligned. The alignments that are classified as genomic are not aligned with uLTRA. uLTRA then proceeds to align all remaining reads. Instead, uLTRA will report minimap2’s primary alignments for these reads. uLTRA then proceeds to align all reads not classified as genomic as described. In a final step, uLTRA compares the reads that have been aligned with both aligners and selects the best alignment based on edit distance to the genome. The final output SAM-file consists of the best alignments to uLTRA-indexed regions and minimap2’s alignments of genomic reads. As most genomes have incomplete exon annotations, the minimap2 wrapping-mode is the default setting to uLTRA. In cases where the annotation is guaranteed to be complete, such as for smaller organisms or controlled SIRV datasets, uLTRA can be run as a stand-alone tool.uLTRA can be used as a wrapper around minimap2 to detect alignments outside annotated regions. In this mode, uLTRA first runs minimap2. After reads have been aligned with minimap2, uLTRA parses minimap2’s alignments to find reads with primary alignments outside the regions indexed by uLTRA. A read with more than a fraction of 2.6.1j. This means that the MEM with the closest genomic coordinate is chosen if several exist.In the implementation, the optimal solution instance is found through backtracking. If several possible traceback paths lead to the same optimal value for a given optimal value in the traceback vector, uLTRA will always choose the closest MEM, i.e. the one with the highest index If several optimal chaining solutions are found, i.e. several positions in the vector traceback vector have the optimal value, uLTRA will report all of the solutions by backtracking each instance (as described above). This is not a rare case since there can be identical or highly similar gene copies annotated on the genome that give the same optimal value.Since each read can have several chaining instances to solve, uLTRA pre-calculates the theoretical maximum MEM coverage that an instance can have, which is upper bounded by the sum of all the regions covered by MEMs in the reads. uLTRA then solves the chaining instances by highest upper bound on coverage. If at any point the upper bound drops below a drop-off threshold (parameter to uLTRA) the current best solution uLTRA skips to calculate the rest of the instances. There is also a parameter to limit the number of reported alignments.2.6.2j.MAMs are formed by aligning segments and exons with at least an alignment identity of X% (default 60), and in case of exons between 5 and 8 nucleotides in length, an exact match is required. Exons of 4 bp or less are ignored because of the potential blowup in the number of matches across the read. Similarly to the MEM chaining, the traceback will choose the MAM with the highest index 2.6.3The exons that are included in an optimal solution of the MAM chaining are concatenated into an augmented reference, and the read is aligned to this reference using parasail  in semi-r is the read length, m is the match score . The default setting roughly corresponds to classifying a read as unaligned if it has more than 25% errors, or if a larger segment of the read is from a region that is not included in the indexing.A read is assigned as unaligned if the alignment score is lower than 2.6.4uLTRA outputs alignments in SAM-file format with genomic coordinates as annotated by the transcript database. In addition, uLTRA outputs a transcript annotation of the alignment following the definitions in We evaluated uLTRA against the two state-of-the-art transcriptomic long-read aligners minimap2 and deSALT. We also attempted to evaluate GraphMap2 but were unsuccessful in using the tool see  SA. SeveWe used three simulated, one synthetic and two biological datasets from both ONT and PacBio Iso-Seq  to evaluWe used three in silico datasets to test the alignment accuracy in a controlled setting . First, For SIM_ANN, which contains simulated reads from annotated transcripts, uLTRA and uLTRA_mm2 have the highest fraction of correct alignments (93.6% and 94.0%) with a 3.2 percentage point increase compared to the second-best performing tool deSALT_GTF . uLTRA aAs for the SIM_NIC, which contains only reads with novel combinations of exons, uLTRA’s and uLTRA_mm2’s accuracy is as high as for the ENS and SIM_ANN datasets . HoweverWe also compared results in a stringent setting when we did not allow any offset between the true and aligned splice sites . In thisWhile simulated data is good for comparisons due to the availability of ground truth annotations, it does not fully capture the error profiles present in sequencing data. We used a subset of 59 isoforms with distinct splice site positions from the ONT cDNA SIRV dataset  to invesWe also used an Alzheimer brain Iso-Seq dataset (denoted ALZ) and an ONT cDNA sequencing dataset from Drosophila   and around exons . A detailed description is found in We also looked in more depth at the concordance of unique isoforms detected in the data that had FSM predictions . In tota3.4.3We further investigated some of the isoform structures uniquely aligned to by uLTRA_mm2 and observed that in several cases, uLTRA’s alignments were correct. We detected several instances in the ALZ dataset where uLTRA_mm2 aligned to both known and novel isoforms containing small exons that were not detected by other aligners . Several of these uniquely detected isoforms had high read support and came from genes such as AP2, APBB, HNRNPM, DCTN2, PRNP, MICU1, SEPTIN7 and APBB1, that have been studied in relation, or linked directly, to Alzheimer’s disease . We measured user time  and peak memory usage (highest memory usage across the program lifetime). Runtimes using 4 cores are shown in  Splice alignment is an algorithmic problem central for the detection and prediction and quantification of isoforms. We have presented a novel splice alignment algorithm, and its implementation uLTRA. uLTRA aligns long transcriptomic reads to a genome using an annotation of coding regions. In addition, uLTRA can also run as a wrapper around minimap2. In this mode, it refines alignments around gene regions. uLTRA outputs alignments in SAM-format, and classifies the splice alignments according to the classification given in Using simulated data, we demonstrated uLTRA’s increased accuracy over other aligners. Particularly, uLTRA outperformed other state-of-the-art splice aligners when aligning reads to small exons. We also observed that uLTRA had high accuracy on the SIM_NIC dataset while the accuracy of other methods substantially decreased. Furthermore, the accuracy was lower across exons sizes on this dataset . The difWe used synthetic ONT data to investigate the performance of alignment algorithms when aligning reads to known splice sites. Our experiments demonstrate that uLTRA aligns a much higher percentage of reads to known isoforms in the data. This holds true when running uLTRA as a wrapper around minimap2, indicating that uLTRA’s alignments are preferred based on edit distance of the alignments. Furthermore, uLTRA’s FSM alignments are distributed across the 59 isoforms with distinct splice-sites without indication of alignment bias toward specific isoforms as other aligners have .On biological data, we found that uLTRA_mm2 had the most predicted isoforms which were also predicted by one of the other aligners. While there is no ground truth for the biological datasets, the high concordance indicates that the high accuracy we observed for uLTRA_mm2 on simulated and synthetic datasets translates to biological datasets. We also demonstrated several examples where uLTRA aligns reads to the correct isoform structure while the other aligners do not. We showed several examples where isoforms containing small exons were misaligned , where employing junction-specific alignment penalties may lead to concordant but erroneous alignments around junctions , and wheWe observed a large fraction of reads in the biological dataset that came from genomic regions in Homo Sapiens and Drosophila, which are two well-annotated genomes. The large majority of these reads were aligned as NO_SPLICE  and are We noted that the computational bottleneck of uLTRA is the MEM finding using slaMEM, particularly, it is accountable for over 60% of the total runtime when parallelizing over many cores. As this is a modular step in the algorithm, we will continue to explore faster alternatives to generate MEMs, or alternatively, to use other seeding approaches such as minimizers . While tWe present a new splice alignment algorithm and its implementation uLTRA. Our method models splice alignment as a two-pass collinear chaining problem with a novel exon chaining formulation. Our analysis highlights some of the challenges with splice alignment and the current state-of-the-art approaches. We show that uLTRA substantially improves splice-alignment accuracy of long RNA-seq reads using simulated, spike-in and biological datasets. On an Alzheimer Brain Isoform Sequencing dataset from PacBio, we demonstrate several examples where uLTRA aligns reads to previously annotated and novel isoform structures that the other aligners did not detect. This highlights the immediate utility that uLTRA has when profiling a new transcriptome. Furthermore, uLTRA can be used both as a stand-alone aligner and as a wrapper around minimap2 to handle reads aligning to unannotated regions.btab540_Supplementary_DataClick here for additional data file.


However, there are still few open-source comprehensive protein structure prediction packages publicly available in the field. In this paper, we present our latest open-source protein tertiary structure prediction system—MULTICOM2, an integration of template-based modeling (TBM) and template-free modeling (FM) methods. The template-based modeling uses sequence alignment tools with deep multiple sequence alignments to search for structural templates, which are much faster and more accurate than MULTICOM1. The template-free (ab initio or de novo) modeling uses the inter-residue distances predicted by DeepDist to reconstruct tertiary structure models without using any known structure as template. In the blind CASP14 experiment, the average TM-score of the models predicted by our server predictor based on the MULTICOM2 system is 0.720 for 58 TBM (regular) domains and 0.514 for 38 FM and FM/TBM (hard) domains, indicating that MULTICOM2 is capable of predicting good tertiary structures across the board. It can predict the correct fold for 76 CASP14 domains (95% regular domains and 55% hard domains) if only one prediction is made for a domain. The success rate is increased to 3% for both regular and hard domains if five predictions are made per domain. Moreover, the prediction accuracy of the pure template-free structure modeling method on both TBM and FM targets is very close to the combination of template-based and template-free modeling methods. This demonstrates that the distance-based template-free modeling method powered by deep learning can largely replace the traditional template-based modeling method even on TBM targets that TBM methods used to dominate and therefore provides a uniform structure modeling approach to any protein. Finally, on the 38 CASP14 FM and FM/TBM hard domains, MULTICOM2 server predictors  were ranked among the top 20 automated server predictors in the CASP14 experiment. After combining multiple predictors from the same research group as one entry, MULTICOM-HYBRID was ranked no. 5. The source code of MULTICOM2 is freely available at  Various computational methods for protein structure prediction have been proposed, which can be largely classified as template-free modeling  and template-based modeling  methods. When significant structural templates and good template-target sequence alignments are available for a target, template-based modeling methods can generate accurate models for the target. But when there are no good templates, template-free modeling methods are the only viable choice for constructing good structural models without referring to known protein structure templates. Template-free modeling had been studied for decades and progressed very slowly until some breakthroughs in the several few years. The breakthroughs were enabled by two major technical advances, leading to the accurate model prediction for both template-based and template-free targets by template-free modeling methods such as AlphaFold3. One advance is the residue-residue co-evolution analysis that provides informative features to improve residue-residue contact/distance predictions for guiding template-free structure reconstruction. Another one is the application of deep learning to protein contact/distance prediction, which dramatically improves the accuracy of contact/distance prediction and therefore substantially enhances the distance-guided template-free modeling6.Building the high-quality structure of a protein from its sequence is important for studying protein function and has important applications in protein engineering, protein design and drug design. Because the expensive, time-consuming and low-throughput experimental methods for determining protein structures  can only be used to solve the structures of a small portion of proteins, fast high-throughput computational protein structure prediction is necessary for constructing structures of the majority of millions of proteins in the nature8. Despite the substantial advances in the field, there are still very few state-of-the-art automated open-source template-based and template-free modeling system available for the community to use9, hindering the development of new methods and application of protein structure prediction in biomedical research and technology development. Here we introduce our latest open-source protein structure modelling system MULTICOM2 that was recently benchmarked in the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14) in 2020. Compared to its previous version (MULTICOM1) tested in CASP1310, its template-based modeling method is leaner, more accurate and much faster. Its distance-based template-free modeling method uses our newly developed deep-learning-based inter-residue distance prediction method—DeepDist11 and is much more accurate than the contact-based tertiary structure modeling based on CONFOLD212 in MULTICOM110. The server predictors based on MULTICOM2 generated structural models with correct topologies for almost all TBM targets and most FM and FM/TBM targets in the CASP14 experiment and were ranked among the top CASP14 server predictors.Most recently, the attention mechanism integrated with deep learning was applied to predict residue-residue distances and even 3D coordinates of atoms, which further enhanced the prediction accuracy and interpretability13 and TM-score14 are the two standard metrics to evaluate the model quality. The value of TM-score ranges from 0 to 1. A TM-score greater than or equal to 0.5 indicates the predicted model and the native structure have the same fold topology, while a TM-score less than 0.17 means no structural similarity between the predicted model and the native structure. GDT_TS score ranges from 0 to 100% (or simply from 0 to 1), a higher value indicating better model accuracy. Both GDT_TS and TM-score measure the global backbone similarity between a model and the native structure without considering side chain atoms.The global distance test score (GDT_TS)Table The average TM-score of the pure ab initio predictor—MULTICOM-DIST on the TBM domains is 0.702 for one trial, close to that of MULTICOM-HYBRID and MULTICOM-DEEP, indicating that the performance of the distance-based template-free modeling method has reached the performance of the template-based modeling method on the TBM domains. It is remarkable progress considering the substantial gap between the two just a few years ago. Moreover, on the 38 very hard FM and FM/TBM domains, the average TM-score of the three predictors is very close and ranges from 0.512 to 0.514 for one trial. The success rate of MULTICOM-HYBRID on the FM and FM/TBM domains is 55% for one trial. For five trials, its success rate on the FM and FM/TBM domains increases to 58%. MULTICOM-DIST and MULTICOM-DEEP’s performance on the hard domains is similar to MULTICOM-HYBRID, which is not surprising because they are based on the same template-free modeling pipeline of MULTCOM2 for FM and FM/TBM domains.15, considering three complementary metrics including GDT_TS13, Quality Control Score(QCS)16 that measures the assembly of secondary structure elements and Molprobity17 that evaluates the quality of all the atoms of a model including side chain atoms. The raw score of a model is the weighted average of the three metrics . And it is converted into a Z-score based on the raw scores of all the models for a target. A Z-score less than − 2 is discarded in calculating the summation of Z-scores on all the targets18. Since MULTCOM2 is an automated prediction system, we evaluated its performance together with other CASP14 automated server predictors on 38 FM and FM/TBM (hard) domains, excluding CASP14 human predictors involving human intervention in prediction.To fairly compare different predictors that may perform well according to different evaluation metrics, the CASP14 assessors used the summation of Z-scores over the CASP14 targets to rank them10 (results not shown). If the multiple predictors from the same research group  are combined into one entry represented by one predictor with the best performance , MULTICOM-HYBRID representing the MULTICOM2 Group is ranked No. 5. Although the difference in the relatively Z-score of the top five groups is pronounced, the absolute average quality score  of these predictors is much closer. As shown in Fig. On the 38 CASP14 hard (FM and FM/TBM) domains, all the three MULTICOM2 server predictors, MULTICOM-HYBRID, MULTICOM-DEEP, and MULTICOM-DIST are ranked among the top 20 automated server predictors based on the CASP14’s official sum of Z-scores , the top templated-based models from the template-based modeling branch have the quality comparable to that of the final top models predicted by the integrated system. For MULTICOM-HYRBID, the average TM-score of top 1 models from the template-based branch and the integrated system are 0.738 and 0.731, respectively. For MULTICOM-DEEP, the average TM-score of top 1 models from the template-based branch and the final integrated system are 0.739 and 0.740, respectively. In this situation, the template-based models sometimes are selected as top-1 models of the integrated system. When the top template hits have E-value between 10−50 and 1, the average TM-score of top templated-based models from the template-based modeling branch is 0.642 for MULTICOM-HYBRID_TBM and 0.648 for MULTICOM-DEEP_TBM, which is much lower than the scores (0.730 and 0.746) of top models of the integrated systems (MULTICOM-HYBRID and MULTICOM-DEEP), indicating that the template-based modeling has a much less impact in this situation. When the top template hits have E-value ≥ 1 , the average TM-score of the top-1 models from MULTICOM-HYBRID and MULTICOM-DEEP is 0.730 and 0.692, much higher than 0.473 and 0.452 of the top-1 template models from the template-based modeling branch, indicating that the template-based models are not useful in this situation. The detailed per-domain comparison is shown in the Supplementary Table To analyze the impact of the template-based modeling branch of MULTICOM2, we compared the top-1 ranked models from the two integrated methods (MULTICOM-HYBRID and MUTICOM-DEEP) and top-1 ranked template-based models built by their templated-based modeling branch. We found that E-value  of the top template from homology search could be one simple criterion to assess if the templated-based modeling branch can contribute to the final prediction. According to the results on CASP14 TBM domains in Fig. 19 or more precisely quality of multiple sequence alignments (MSA) and the accuracy of the inter-residue distance prediction are critical for the template-free modeling branch and the entire MULTICOM2 prediction system. Fig. 20; L: sequence length) and the quality of the top-1 models. Pearson’s correlation coefficient for MULTICOM-DIST, MULTICOM-HYBRID, and MULTICOM-DEEP is 0.67, 0.70, and 0.70, respectively. The detailed results for each CASP domain are shown in the Supplementary Table While the significance of templates determines the quality of template-based models and their contribution to the whole system, the number of effective sequence (Neff)It typically requires 12 hours for a protein of typical length  for MULTICOM2 to complete the entire modeling process on an Intel(R) Xeon(R) CPU E5-2660 v3 10-core processor, which is about two times faster than MULTICOM1. With hundreds of cores available on a computer cluster, MULTICOM2 can be used to predict structures of many proteins per day and therefore is applicable to the genome-level protein structure modeling.8 in CASP14 demonstrates that an end-to-end deep learning system with the attention mechanism can directly predict the tertiary structure of a protein from its multiple sequence alignment, skipping the distance-to-structure conversion. In the future, we plan to develop deep learning-based end-to-end protein structure prediction methods and add them into the next release of MULTICOM.We develop and release our latest automated protein structure prediction system (MULTICOM2) as an open-source software package for the community to use. MULTCOM2 is leaner, more accurate, and much faster than MULTICOM1. It was ranked among top automated server predictors in the latest 2020 CASP14 experiment. It can predict correct folds for almost all regular targets and a majority of very hard targets. Its template-free modeling without using any known structures as templates can work well on both regular template-based modeling targets and hard template-free modeling targets, which provides a uniform, reliable approach to modeling the structure of any protein. Therefore, it can be a useful tool for both the current development and application of protein structure prediction methods. While the inter-residue distance prediction can be converted to tertiary structures, significant modeling effort is still needed to do the conversion. The recent success of AlphaFold2Figure 21, PSI-BLAST1, and HMMER22. Each profile is searched against a template library in order to identify a list of structurally similar templates and their sequence alignments with the target sequence. The pairwise target-template alignments are combined into multi-template alignments between the target and the multiple templates if the structures of the templates are consistent24. Multi-template alignments along with the template structures are fed into Modeller25 to build the structural models for the target protein. Different from MULTICOM110 that uses more than a dozen sequence alignment tools and relatively slow threading tools , MULTICOM2 only uses HH-suite21, PSI-BLAST1, and HMMER22 to build profiles, identify protein templates and generate target-template sequence alignments. Among those tools, hhblits and hhsearch in HH-suite are much more sensitive than PSI-BLAST and HMMER and play an essential role in the template identification. Therefore, the template search process in MULTICOM2 is much faster than MULTICOM1. In order to further improve the sensitivity of template identification, MULTICOM2 applies DeepMSA32 to search the target sequence against large sequence databases  for generating the deep multiple sequence alignments, which are used to build profiles to identify templates for template-based modeling.Given a target sequence, MULTICOM2 searches it against the non-redundant protein sequence databases to build target sequence profiles such as position-specific scoring matrices (PSSM) and hidden Markov models (HMMs) using HH-suite35 to predict inter-residue distances. The predicted distance maps are used by DFOLD36  and trRosetta6 to generate template-free models. DFOLD differs from CONFOLD2  that takes binary contacts as input to build tertiary structures. To use trRosetta, the distance maps predicted by DeepDist are used to substitute the default distance maps generated by trRosetta from the multiple sequence alignments before ab initio tertiary structure modeling. The DeepDist’s distance prediction is also used to select templates based on their matching with the predicted distance maps for template-based modeling. By default, about 100 template-free and template-based models are constructed for a target. The models are ranked by three different model quality assessment methods: APOLLO38 of ranking models based on their pairwise structural similarity, SBROD39—a single model energy function of ranking models, and the distance-based ranking based on the similarity between the predicted distance maps and the distance maps of a model40. Any one of the three rankings or their consensus can be used to select top models for the target.In parallel to the template-based modeling, the target sequence is also used as input for a deep learning distance predictor—DeepDist25 or AIDA41.Based on the target-template sequence alignment generated by HH-suite, MULTICOM2 also predicts if a target needs to be split into multiple domains or modeling units. If some region (> 40 residues) of a target does not have significant templates but other regions have, it will be split into multiple template-based and template-free domains according to the alignment. The structural models for each domain are then predicted by the same pipeline above. The top five ranked models from each domain are joined into full-length models for the multi-domain target by Modeller38 to rank models, whereas MULTICOM-DEEP primarily used the average ranking of SBROD39 and the distance-based ranking (the matching between a model and the predicted distance map) to select final models. MULTICOM-DIST skipped template-based tertiary structure modeling entirely and only used the template-free modeling to generate tertiary structure models and used SBROD39 to rank them, even though it still used template-target alignments to identify domain boundaries if needed.In order to evaluate different modeling options of MULTICOM2, three predictors based on MULTICOM2—MULTCOM-HYBRID, MULTICOM-DEEP, and MULTICOM-DIST participated in the CASP14 experiment. MULTICOM-HYBRID and MULTICOM-DEEP used both the template-based and template-free modeling and differed only in model ranking. MULTICOM-HYBRID primarily used APOLLOSupplementary Information.


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.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.https://www.igs.umaryland.edu/labs/neuwald/software/compassThe eCOMPASS executable, C++ open source code and input data sets are available at Bioinformatics online. Protein sequence analyses, and particularly those that are statistically based, often rely upon very large multiple sequence alignments (MSAs), consisting of tens or hundreds of thousands of sequences belonging to a large superfamily. Using such an alignment increases the statistical power and breadth of an analysis and, by partitioning the MSA into hierarchically arranged subgroups based on subgroup-specific patterns , one canWe define an accurate alignment to be one that reflects sequence homology. A more accurate MSA should reveal evolutionarily conserved structural and functional constraints better than a less accurate one. In large, diverse sequence sets such constraints become more statistically evident, thereby allowing subtly conserved homologous regions to be identified and aligned, as illustrated in Because obtaining a highly accurate MSA typically requires manual curation, we have developed and applied the Multiply Aligned Profiles for Global Alignment of Protein Sequences (MAPGAPS) program , which u‘gold standard’ alignment of the subset of sequences with known structure. Furthermore, it requires no knowledge of how the MSAs were produced, nor upon how the methods that produced them perform on other sets of sequences. Rather, for each MSA, it first derives, from pairwise correlations among columns, internal evidence of likely 3D contacts among residue positions of the aligned proteins, and then uses the known structures to assess the relative accuracy of this evidence. This approach is based on the principle that, to maintain a protein family’s structural fold, interacting residues pairs tend to coevolve, resulting in correlations better seen within accurate alignments. Hence, the degree to which 3D contacts may be correctly inferred from an MSA depends upon its accuracy.Here, we introduce eCOMPASS, a program that evaluates the relative accuracy of two MSAs of the same large set of sequences by applying direct coupling analysis (DCA) based upon pseudo-likelihood maximization in conjunction with a procedure to estimate statistical significance. It requires as input only the MSAs themselves and structural coordinates for a minimum number  of the aligned sequences. It does not rely upon any set of benchmark alignments, nor even upon a Because eCOMPASS applies to the evaluation of the overall quality of specific sequence alignments that are very large, it cannot be readily evaluated using known benchmark MSAs, nor are we aware of previous approaches to which it can be properly compared. We therefore argue for its validity from its inherent plausibility, its application to simulated gold standard alignments, and its consistency with a completely independent measure of alignment accuracy than the measure eCOMPASS deploys.We first describe the eCOMPASS algorithm and illustrate its use by applying it to eight pairs of large MSAs obtained from the CDD and PFAM databases and containing a sufficient number of proteins of known structure. We also describe the sort of insights eCOMPASS can reveal regarding the relative quality of such MSAs. Second, we validate it on simulated MSAs generated from realistic Potts models of protein superfamilies versus realignments of the simulated sequences using four different alignment methods. Third, we evaluate its robustness to changes in various hyperparameter settings.eCOMPASS takes as input two MSAs of the same set of protein sequences aligned using two different methods. We recommend that the set include at least 10 proteins of known structure. The method’s basic strategy is, first, to use correlations among columns in each MSA to predict which pairs of columns correspond to residue 3D contacts; and then to check the accuracy of these predictions (measured as described below) using the aligned proteins of known structure. The method assumes that the more accurate the overall MSA, the more accurate will be structural predictions derived from its column correlations. Evidence for the validity of this assumption is provided through analyses of simulated MSAs.‘gold standard’ alignments whose accuracy must be assumed. It bases its evaluation only on the given MSAs and on the experimentally determined structures.Note that, although eCOMPASS uses a relatively small number of sequences with known structure to vote on the relative accuracy of two MSAs, each structure’s vote is based upon evidence derived from all the sequences in each of the MSAs. Thus, an MSA that accurately aligns the structures in question to one another but does a poor job of aligning sequences from a much larger and more diverse protein superfamily, should fare poorly in eCOMPASS’s estimation. This contrasts with evaluation methods that use the accurate alignment of a  test set alone as a proxy for an MSA’s more general accuracy. Note also that eCOMPASS requires no i and j and at positions j and k, then residues at positions i and k may also correlate even though they fail to interact directly. DCA overcomes this problem by disentangling direct from indirect correlations using a variety of algorithmic strategies. eCOMPASS uses pseudo-likelihood maximum entropy optimization  scores for all of M1’s column pairs. DC scores correspond to the average product corrected Frobenius norms  intervening residues within the protein’s primary sequence. (We impose this latter condition because we are not interested in predicting close contacts that are imposed by a protein’s backbone.) R. Note, however, that each individual structure defines distinct We address this issue by using the sequence of each protein with known structure, considered individually, to choose comparable subsets of M1 and M2 that are used to construct first K1 and K2, and then Our approach is based on the assumptions that within a protein family the evolution of structurally interacting residue pairs is likely to be correlated, and that an accurate multiple alignment of sequences in the family should capture information concerning such correlations in the form of high DC scores. Given two MSAs for a protein family, and a particular structure, we have constructed sets of DC scores, L elements, among which D are designated as distinguished, and seeks the initial segment of the array, of length X, with the most surprising number d of distinguished elements, as measured by a P-value. A generalization of ICA that has been applied to DC scores  . ICA conC scores , and whi≤z (with z = 4 Å by default). Note that, except for glycine, z is based on the distance between sidechain atoms rather than between α- or β-carbons. ICA returns an S-score .Here, we take the array of elements to be the set of DC scores  S-score  calculatM1 and M2 by evaluating solely how well each MSA aligns the reference structures to one another. However, this would ignore how the vast number of remaining sequences are aligned. In contrast, eCOMPASS measures how well the DC scores derived from each MSA predict 3D contacts between residue pairs in each reference structure. This assumes, however, that each structure is properly aligned, in the main, within both MSAs, which may not be the case.It would be possible to assess the relative quality of i, the subset iR of its residues that are aligned by the MSA to residues rather than null characters in all other structures; note that the iR will be of the same size for all structures. We then compute, for each pair of structures i and j, the quantity Δ𝔇ij, defined as the mean, for all pairs of residues a and b within iR, of the absolute difference between the Cα distance of a to b and the Cα distance within structure j of the residues to which a and b align. It can be seen that Δ𝔇ij = Δ𝔇ji, and this quantity may be understood to measure how well sequences i and j are structurally aligned with one another . After all structures with questionable alignment within either MSA have been removed, we calculate M1 and M2.To identify reference structures that may be misaligned within a particular MSA, we first determine, for each structure i varies from one MSA to another, as of course do the subsets of residues iR within the various structures. Thus, in contrast to the iS, the Δ𝔇i are properly comparable only among different structures for the same MSA, but not between one MSA and another. Nevertheless, as we will see below, there is a noticeable tendency for the MSA preferred by the measure i), which can be understood as a rough measure of how well an MSA aligns the reference structures to one another.Note that the number of columns used to calculate the Δ𝔇We created a Potts model for each of 40 CDD/MAPGAPS-generated MSAs  to 8 CDD versus PFam MSAs; (ii) to 40 realigned versus gold standard simulated MSAs; and (iii) to 31 CDD versus JackHMMER MSAs using various eCOMPASS hyperparameter settings.S-scores than Pfam MSAs  Note that this has the effect of eliminating the one negative To illustrate and study our procedure for excluding structures, we consider in detail its operation on the PTS domain. In i for 3czcA is no longer an outlier, and the It is not eCOMPASS’s function to amend the MSAs with which it is supplied. However, to study further the validity of eCOMPASS’s procedure for rejecting structures as misaligned, and their corresponding sed Dali  to struci for one MSA may decrease N1 versus N2. There appears to be a slight tendency for both 10(P) to decrease with the inclusion of all structures, but this is neither systematic nor coordinated. The advantage of excluding apparently misaligned structures is that this focuses more on the overall quality of the MSAs, as measured by their DC signal, and less on the alignment accuracy of the relatively small number of structures considered. To help assess such distinctions, eCOMPASS computes results using both approaches.One may object to our procedure for excluding a structure, from one or both MSAs, based upon internal evidence that it has been misaligned. Such a structure generally represents not only itself but also the alignment of closely related sequences, and arguably should have a vote equal to that of other structures regarding which alignment is better. In P-value. For example, for the sulfatases (SFTS) P = 0.06 and, among the retained For some superfamilies neither MSA was significantly favored based on the binomial i (2.99 Å) for MSA 1  , Kalign P < 10−5. Among these the CDD MSA was significantly better at the P < 10−3 level for 12 domains whereas the JHM MSA was significantly better for 6 domains  . To redu domains .P-values of 0.005 and 0.002.To evaluate the robustness of eCOMPASS, we reran each of these analyses using various CCMpred hyperparameter settings.  Using either flat (uniform) priors or Jeffreys uninformed priors [28] yielded essentially identical results . We alsoThe observed variability in the binomial probability yielded by different parameter settings is likely due to changes in the implicit nature of the MSAs, of the ICA array or of both. For example, decreasing the CCMpred reweighting threshold (eCOMPASS computes a statistical score (Almost all multiple alignment construction methods employ some objective function of alignment quality which they attempt to optimize. For assessing the relative accuracy of two multiple alignments, relying upon the objective function used for either's construction will of course bias the results, so it is best to seek an independent measure. The congruence of structural contacts with alignment-derived DCA scores provides a convenient such measure, and one that avoids reliance upon a set of gold standard alignments.Several recent multiple alignment construction methods  incorporS, for the relative accuracy of the two alignments. Following, for example, the approach of P-values for the difference of these scores. Whether such a statistical approach is superior to the one taken here is an avenue for further study.Recently, Ideally, eCOMPASS should be applied using a set of reference structures representing diverse subgroups within a superfamily, as in the examples here. Then, in addition to providing an assessment of overall alignment accuracy, eCOMPASS can identify those subgroups that are least accurately aligned, as an aid to improving MSA methods. This raises the issue of multiple conformations for the same protein, which is a major concern for DCA. A future version of eCOMPASS might provide the option of choosing the highest DC score among alternative conformations for each residue pair. In order to investigate directly coupled residue pairs corresponding to a subgroup-specific conformation, such as we reported recently (For MSA methods that fail to incorporate information from DCA into their objective functions, the statistical significance of the agreement between DC scores and 3D contacts within available structures serves as a measure of alignment accuracy that is independent of the criteria used in constructing the MSA. In any case, eCOMPASS should be uniquely useful for evaluating the extremely large MSAs typically required for deep learning protein sequence analyses and for statistical analyses requiring a vast amount of sequence data.btab374_Supplementary_DataClick here for additional data file.


We are at the beginning of a genomic revolution in which all known species are planned to be sequenced. Accessing such data for comparative analyses is crucial in this new age of data-driven biology. Here, we introduce an improved version of DIAMOND that greatly exceeds previous search performances and harnesses supercomputing to perform tree-of-life scale protein alignments in hours, while matching the sensitivity of the gold standard BLASTP. An updated version of DIAMOND uses improved algorithmic procedures and a customized high-performance computing framework to make seemingly prohibitive large-scale protein sequence alignments feasible. Current sequence search algorithms and software tools would be impractical for analyzing data of this magnitude when aiming to retain sensitivity similar to the gold standard BLASTP3. In an experimental study we estimated that querying the National Center for Biotechnology information (NCBI) non-redundant (nr) database (~280 million sequences) against the UniRef50 database (~40 million sequences) using BLASTP would require more than 2 months even on a supercomputer equipped with 20,800 cores (http://www.diamondsearch.org).Within the next decade, The Earth BioGenome Project00 cores . Howeverk-mer information for annotation and classification4, most functional and phylogenomic studies rely heavily on high alignment sensitivity to obtain useful insights about the functional conservation of proteins and their evolutionary divergence along phylogenetic lineages. For data-intensive studies in these fields, BLAST remains the tool of choice due to its paramount alignment sensitivity.DIAMOND is a fast and sensitive protein aligner that was initially developed for metagenomics applications to achieve ultra-fast alignments at the cost of alignment sensitivity, compared with the gold standard, BLAST. Although DIAMOND is proven to be practical for many metagenomics studies that also often rely on 5, gene age inference7, and functional annotation of genes and gene families8 to be carried out with the same accuracy as BLAST, but with an 80–360-fold computational speedup. In --ultra-sensitive mode, DIAMOND (v2.0.7) achieves this BLAST-like sensitivity milestone while reducing the computational run time of BLASTP-heavy studies from months to hours.Here, we introduce a greatly improved version of DIAMOND that provides two sensitivity modes, --very-sensitive and --ultra-sensitive, which will enable data-intensive comparative genomics research such as tree-of-life scale tracing of protein evolution3), USearch (ref. 9), LAST (ref. 10) or MMSeqs2 (ref. 11) are also optimized to run fast protein alignments, but still require longer computation times and, with the exception of BLAST, are less sensitive than DIAMOND when dealing with very large datasets. These tools already experience limitations when they try to handle searches at the scale of the NCBI nr database, which currently contains the largest collection of sequences, representing genomic information for ~12,000 eukaryotic species. Therefore we sought to build a protein search infrastructure that can accommodate the demands of sensitive homology searches on this exponentially growing dataset of sequenced species.This version of DIAMOND is different from other protein aligners and its older versions in that it focuses on ultra-fast but sensitive protein searches that can scale with sequencing efforts; for example, to meet the demands of the large-scale Earth BioGenome Project and analogous bulk sequencing projects. Alternative tools such as BLASTP  and cloud computing by providing a powerful distributed computing implementation customized for large-scale protein searches, incorporating our new DIAMOND search scheme . In summ12 (http://scop.berkeley.edu/)  following the same procedure to serve as a reference database for the benchmark.We demonstrate the search capabilities of DIAMOND (v2.0.7) by systematically comparing its performance against BLASTP v2.10.0) and MMSeqs2 (release 11), and against an older version of DIAMOND (v0.7.12), all of which are currently the most promising alternatives for sensitive tree-of-life scale protein searches  . Establi15 used small benchmark datasets for the comparison with DIAMOND. As stated earlier, DIAMOND is optimized for searches using large query and reference databases. Therefore valuable benchmarking insights can only be achieved when comparing DIAMOND and other tools using large benchmark datasets, rather than focusing on small query or reference examples.It is important to note that some previous performance benchmarks between older versions of DIAMOND and other aligners16 (v0.7.12) we observed a 6–8-fold speedup, while the old DIAMOND was also far behind the other benchmarked tools in terms of sensitivity. When comparing DIAMOND (v2.0.7) to BLASTP (v2.10.0) we observed an ~8,000-fold speedup when using the least sensitive mode, and still an 80-fold speedup when running DIAMOND (v2.0.7) with a sensitivity level matching that of BLASTP (ultra-sensitive mode). Closer inspection of the trade-off between sensitivity and specificity on the basis of ROC curves  in all four sensitivity modes using our SCOPe-annotated benchmark dataset as a query against the UniRef50 database, and compared its computational performance and level of sensitivity against analogous runs performed with BLASTP v2.10.0), MMSeqs2 (release 11) and DIAMOND (v0.7.12). Figure 0.0, MMSeves Fig.  shows thves Fig.  enables In addition, we compared older versions of BLASTP  to the 2019 version of BLASTP (v2.10.0) and found that the 2019 version of BLASTP was fourfold faster than its 2015 version. Although this speedup is impressive, we are not able to envision a scenario in which this rate of increase will enable tree-of-life scale protein alignments when dealing with sequences from millions of eukaryotic species.To demonstrate the capabilities of our tool when supported by an HPC infrastructure, we aligned all 281 million protein sequences from the NCBI nr database against the UniRef50 database, which consists of 39 million sequences, using DIAMOND (v2.0.7) in ultra-sensitive mode on the Cobra supercomputer of the Max Planck Society. This comprehensive comparison across all domains of life was computed in less than 18 hours using 520 compute nodes  that enables users to handle the accelerating growth of sequence information for data-driven comparative and functional genomics studies. We designed this framework to meet the computational demands of future high-sensitivity sequence searches, to gain fundamental insights into protein evolution and molecular phylogenetics.17, to optimize the handling of large query and large reference databases. In the first step, tables of seed–location pairs are built for query and reference sequences. Next, matching seeds are computed using a hash join technique that conducts recursive radix clustering of both tables until a hash table for the query data fits into the cache, at which point the rest of the join is computed by hashing18. We found this approach to be faster than the sorting method used by older versions of DIAMOND16, especially given that a full sorting of the reference table is avoided for smaller query datasets.DIAMOND uses the double-indexing approach, in conjunction with multiple spaced seedsThe double-indexing algorithm is designed to be cache aware, given that the data associated with one seed need to be loaded for comparison from memory only once, while the classical index-based linear seed lookup suffers from poor data locality. Additionally, our on-the-fly indexing method enables efficient use of multiple spaced seeds by processing the shapes one at a time and not requiring the index tables for all shapes to be present in memory simultaneously, while also avoiding expensive seed lookups through our cache-friendly hash join implementation.19. Even when operating with 64 shapes, the run time generation of the indices, together with the join computation, take up less than 1% of the total run time of the program. When processing the NCBI nr database, the total size of these indices would be 123 billion letters × 9 bytes per entry × 64 shapes, which is ~64 TB if kept in memory or written to disk, while DIAMOND (v2.0.7) requires less than 16 GB of RAM when running in ultra-sensitive mode. This shows that DIAMOND does not require expensive computing infrastructures and can be operated with modest hardware resources if needed. Because of the runtime indexing, DIAMOND maintains disk-based database files that contain only the reference sequences, and can optionally also use BLAST databases (since v2.0.8).DIAMOND (v2.0.7) uses two seed shapes of weight 10 for its fast mode, 16 shapes of weight 8 and 14 shapes of weight 7 for its sensitive and very-sensitive modes, respectively, and 64 shapes of weight 7 for its ultra-sensitive mode. The seed shapes were computed using SpEEDIn the first stage of the sequence comparison process, a hamming distance computation between a query sequence and a subject sequence is performed at all seed hit locations in a 48-letter window encompassing the hit. We optimized this procedure using a chain of SSE (streaming single-instruction multiple-data (SIMD) extensions) pcmpeqb, pmovmskb and popcnt instructions to achieve a tenfold decrease in computation time compared with an ungapped alignment incorporating a scoring matrix, while reducing the number of hits by 1–2 orders of magnitude. A sensitivity-level-dependent cut-off for the hamming distance that can also be manually set by the user determines whether a hit is passed to the next filter stage.16, and maximize the filtering throughput by using a loop-tiling strategy to incorporate the cache hierarchy and address the fact that the data associated with a single seed may exceed the cache capacity in the new very-sensitive and ultra-sensitive modes of DIAMOND (v2.0.7). We also load the 48-letter windows at the query and subject locations into linear buffers prior to running the all-versus-all hamming distance computation, to make best use of the hardware prefetcher and to avoid any random memory access.We further extend our initial approach, introduced in the original version of DIAMONDe-value threshold determines the hits that will be passed to the next stage.After the hamming distance stage, the next step in the pipeline computes ungapped extensions at the seed hit locations. This procedure is vectorized using AVX2 instructions, aligning one query against up to 32 subject sequences. After 32 subject sequences are loaded into AVX2 registers, a 32 × 32 byte matrix transposition is computed using a series of 160 unpack instructions, such that 32 letters of different subjects are interleaved into one SIMD register, and the match scores can be loaded along the query. A sensitivity-level-dependent Due to its double-indexing algorithm, DIAMOND may find the same alignment multiple times independently during the search stage. These redundant hits need to be filtered out to avoid an excessive use of temporary disk space. DIAMOND accomplishes this task by inspecting the local ungapped alignment for seed hits to the left of the hit that is currently being processed, as well as seed hits by previously processed shapes. If such a hit is found, DIAMOND notices the repetition and the current hit is discarded. Given that this procedure entails checking against up to 64 different seed shapes, we further optimized this process by incorporating a precomputed lookup table that stores information on whether any of the processed shapes will hit a given bit-encoded match or mismatch pattern, thus enabling the same check to be performed in one pass over the local hit pattern.Given that the typical application of an aligner will require the reporting of a certain number of best alignments (hits) for each query (as set on the command line using the --max-target-seqs option), DIAMOND makes use of this parameter to control the computational effort spent on seed extension and avoid having to compute gapped extensions for all seed hits. To this end, after the seed search within target sequences has been concluded, we determine a tentative order of target hits with respect to a single query. In the present case, this ranking procedure uses the ungapped extension scores at seed hits to assign a linear order to the targets. DIAMOND sorts the target list by ungapped extension score (from best to worst) for each target, similar to the way in which MMSeqs2 uses its ungapped extension-derived prefilter scores. Although MMSeqs2 will then compute Smith–Waterman extensions for a fixed number of best targets (as set using the --max-seqs parameter), DIAMOND uses a dynamic criterion to halt evaluation of further targets. We refer to this dynamic approach as adaptive ranking, which improves the DIAMOND reporting accuracy compared with the static criterion used by MMSeqs2, while providing a less biased and more data-adapted filtering procedure. The ranked list is processed in chunks of 400 targets (configurable on the command line using ext-chunk-size), for which extensions are computed. If no extension in the current chunk yields a significant alignment under the user-specified reporting criteria, computation of further extensions for the query is aborted, otherwise the next chunk of targets will be processed.20 extensions is expensive, we have developed a fast heuristic algorithm designed to estimate a gapped alignment score and discard hits that most probably do not meet the user-set reporting threshold. We use a query profile data structure in the same way as the vectorized Smith–Waterman algorithm introduced by Farrar21, which is an array for each of the amino acid letters that stores the scores along the query against the given residue. We then use AVX2 instructions to sum up these scores along diagonals of the dynamic programming matrix, thus computing local ungapped extension scores on diagonals. This approach ignores gaps in the alignment and therefore eliminates intra-register data dependencies. With its minimal logic, our heuristic achieves a throughput ~fivefold faster than a Smith–Waterman computation using the vectorized SWIPE method22. Nevertheless, ungapped scores on the diagonals can be used to estimate a gapped extension score by thresholding and computing a one-dimensional dynamic program that disregards the location of the diagonal segments. Although this simplifying assumption leads to an overestimation of the true alignment score most of the time, the heuristic is still able to reduce the number of spurious hits by one order of magnitude in the most sensitive alignment mode. If required by the user, this filter step can be disabled using the option gapped-filter-evalue 0.Given that computing full Smith–Waterman23). DIAMOND (v2.0.7) introduces the use of chaining on protein sequences. The result of the chaining computation is used to infer a scaffold for the optimal alignment and to determine the band geometry for a banded Smith–Waterman algorithm20.Chaining is the computation of a dynamic program at the level of diagonal segments instead of at the base or residue level, and has been used successfully in DNA alignment tools such as minimap2  by computing pairwise connections between the diagonal segments in one left-to-right pass. Such pairwise connections are then stored as graph edges, incorporating their inbound and outbound coordinates to prevent invalid chains and to allow zigzag connections in which the optimal path repeatedly shifts between the same two diagonal nodes. A red–black tree for the nodes ordered on the diagonal is used to quickly access the most proximal nodes and candidates for determining a connection. For each node, the best score of a local alignment ending in that node is stored, the maximum of which yields the final score estimate and end point for backtracing of the approximate optimal alignment.22) approach that accommodates banding. Due to their design, both the SWIPE and the ‘striped’ SIMD vectorization21 algorithms do not easily allow banded alignment, resulting in the need for an O(n²) computation in proportion to the length of the query and subject sequences. We vectorize the alignment of a query against up to 32 subjects by overlaying the banded dynamic programming matrix columns of the subjects based on their query ranges . Given that the bands of the subjects are different, this cannot be fitted perfectly into the register, but reaches a register load efficiency of 80–90% for larger databases. All extensions are computed using 8-bit scores and are repeated when an overflow is detected, unless an alignment score of >255 is already known from previous stages.The final extensions are computed using a modified version of the vectorized SWIPE  also supports applying the BLAST compositional matrix adjust scoring procedure24 to compute BLAST-like alignment scores (options --comp-based-stats4).Alignments are scored using the BLOSUM62 matrix by default. In addition, we also use a method of composition-based score adjustmentsAs an alternative, DIAMOND (v2.0.7) also includes the option to compute full-matrix instead of banded Smith–Waterman extensions (command line option --ext full), which are also vectorized using the SWIPE algorithm.25 are known to be noisy and contain frequent indel errors, a problem that also translates to assemblies derived from such long reads. In consequence, genes cannot be detected reliably on such DNA sequences. DIAMOND addresses this issue by providing frameshift alignments in translated search (blastx) mode. The protein sequences corresponding to all three reading frames of a strand are aligned simultaneously against the target sequence, allowing shifts in the reading frame at any position in the alignment, while incurring a user-defined score penalty (set using -F on the command line). The raw MinION reads and contigs up to the length of full bacterial chromosomes are supported as input in translated search mode, enabling gene discovery and annotation in the absence of known gene boundaries.Reads produced by MinION technology26, many more and stronger spurious similarities will arise than are expected on random sequences, as defined by an e-value threshold parameter27. DIAMOND reduces this false-positive bias by using more stringent and more sophisticated masking paradigms based on tantan. If required, the tantan masking can be replaced by the more conservative default BLASTP SEG masking and composition-based statistics using the option --comp-based-stats 3 (ref. 24).Differentiating between true evolutionary relationships and spurious similarities presents a big challenge in remote homology detection, particularly given the repetitive nature of sequence regions found in many genomes. When dealing with an increasing load of available genomes for tree-of-life scale sequence searches, the ability to differentiate between similarity relationships based on sequence repetitiveness and homology based on a biologically meaningful sequence structure (non-repetitive sequence under purifying selection) becomes crucial to reduce the number of false-positive hits and increase alignment specificity at scale. Masking of low-complexity regions (repeat masking) is the most commonly used strategy to eliminate false-positive hits and to retain only hits found in biologically meaningful homologs. It has been shown that despite using the SegMasker tool included in BLASTP28, our implementation does not use any special interprocess communication libraries, such as the message passing interface (MPI) specific to HPC environments, instead it relies on input–output operations supported by any POSIX-compliant parallel file system that is mounted on all of the compute nodes involved. The advantage of this approach is that work packages are distributed in a self-organized way at run time to all participating worker processes using simple file-based stacks located in the parallel file system, with atomic push and pop operations. Once all database chunks for a specific query chunk have been processed, the final worker process involved in the query chunk takes on the role of performing the join operation to ultimately create the output stream. Note that the largest part of the temporary files stays local to a compute node, and only the lightweight work-stack files and the DIAMOND hits from the protein searches are written into the shared parallel file system. This strategy significantly reduces input–output overloads and enables massively parallel processing of DIAMOND runs. In addition to the lack of complex dependencies, such as on MPI, we highlight the particular advantages of our approach. First, there is no designated primary worker to induce a bottleneck due to synchronization, or to act as a potential single point of failure. Second, and by design, worker processes may join and leave at run time, which is less important on classical HPC systems that use batch systems to orchestrate potentially large numbers of processes, but is of striking advantage on elastic cloud-computing resources and on existing commodity resources such as networked laboratory desktop computers. Last, our transactional file-based work-distribution protocol enables fault tolerance, which means that if worker processes die unexpectedly, other processes in a subsequent run can take on and resume their work packages.As part of DIAMOND, our comprehensive sequence search framework supports a distributed-memory parallelization to leverage the computing power of state-of-the-art HPC and cloud-computing resources for massive-scale protein alignments. To this end, both the query database and the reference database are segmented into data packages that we refer to as chunks. The Cartesian product of both query and reference sets defines a  set of work packages. In the first instance, files containing metadata on these work packages are created centrally before a parallel run is started on independent computing nodes and are subsequently processed in a distributed manner by multiple worker processes of DIAMOND. Usually, only one worker process runs per compute node, efficiently utilizing all of the locally available cores via threads. Unlike related work such as mpiBLAST22 using an e-value cut-off of 10−5 against the SCOPe ASTRAL40 v2.07 dataset12 of domain sequences consisting of 4,850 protein families, which resulted in a collection of 7.74 million annotated protein sequences. We used the hit with the highest bit score per SCOPe fold  to infer the protein family annotation while allowing multidomain associations.To create a benchmark database, we annotated the 14 September 2019 release of UniRef50 containing 37.5 million sequences with SCOP families. To categorize each protein sequence, we ran SWIPE29 on the sets of all protein sequences annotated with the same superfamily to cluster and reduce them to a maximum of 1,000 sequences, which we selected as representatives of that superfamily, resulting in a benchmark dataset of 1.71 million queries.Given that DIAMOND requires a large query dataset to reach its maximum efficiency, we used an analogous SWIPE approach and annotated the NCBI nr database from 25 October 2019 in accordance with SCOPe families. We used UPGMA clustering30.Both query and reference sequences were locally shuffled in 40-letter windows outside the annotated ranges. All benchmark datasets and annotations have been publishedAlignment for all tools was run on an AMD Ryzen Threadripper 2970WX 24-core workstation clocking at 3.0 GHz with 256 GB of RAM, except for the BLASTP (v2.10.0) run, which, due to its run time limitations on a desktop computer workstation, was performed on the Max Planck Society’s Draco supercomputer at Garching, Germany, using 24 nodes (32 cores on two Intel Haswell E5-2698v3 chips per node). On the benchmark machine the performance of BLASTP (v2.10.0) was estimated using a random subset of 10,000 queries sampled from the initial benchmark dataset.e-value of 1,000 . Only hits until the first alignment against a false positive were taken into account, which was defined as the alignment of query and subject sequences from different SCOPe folds. For multidomain proteins, the AUC1 value was averaged over the domains. The AUC1 values of the individual queries were again averaged over the query dataset to obtain the final sensitivity value Fig. . To ensuof 1,000 . FurtherWe explored the sensitivity of all compared tools in more detail by resolving it at the level of amino acid sequence identity of true-positive alignments. For this purpose, we define the sequence identity of a query–subject association induced by annotation with the same SCOPe protein family as that obtained from the Needleman–Wunsch alignment between the pair of annotated ranges in the query and subject. Extended Data Figure 31  and Illumina HiSeq 2500 paired end sequencing (2 × 250 base pairs). The datasets were created based on data from a recent rumen metagenome study31 , totaling 20,800 computing cores , using the NCBI nr database  as the query database, and UniRef50 as the reference dataset. We randomly shuffled the sequences in both FASTA files to avoid a load imbalance due to a biased distribution of sequences in the original files. As a result, DIAMOND v2.0.0 produced 23.1 billion pairwise alignments in the --ultra-sensitive case and 23.0 billion pairwise alignments in the --very-sensitive case, starting from an initial query dataset that contained 281 million sequences and a reference dataset that contained 39 million subject sequences. In --very-sensitive mode the run terminated in 5.42 hours, while in --ultra-sensitive mode it terminated in 17.77 hours. The latter run is shown in Fig. We envision that in the future this type of DIAMOND output will be easily accessible to all life scientists via a web application in which users can filter and search for their protein homologs of interest within minutes across the tree of life on a precomputed dataset, instead of having to perform complex data analytics and months’ or years’ worth of BLAST searches to obtain sensitive protein alignments at this scale.Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41592-021-01101-x.Supplementary InformationReporting Summary


The functions of RNA molecules are mainly determined by their secondary structures. These functions can also be predicted using bioinformatic tools that enable the alignment of multiple RNAs to determine functional domains and/or classify RNA molecules into RNA families. However, the existing multiple RNA alignment tools, which use structural information, are slow in aligning long molecules and/or a large number of molecules. Therefore, a more rapid tool for multiple RNA alignment may improve the classification of known RNAs and help to reveal the functions of newly discovered RNAs.Here, we introduce an extremely fast Python-based tool called RNAlign2D. It converts RNA sequences to pseudo-amino acid sequences, which incorporate structural information, and uses a customizable scoring matrix to align these RNA molecules via the multiple protein sequence alignment tool MUSCLE.RNAlign2D produces accurate RNA alignments in a very short time. The pseudo-amino acid substitution matrix approach utilized in RNAlign2D is applicable for virtually all protein aligners.The online version contains supplementary material available at 10.1186/s12859-021-04426-8. RNA molecules are central players in various cellular processes, including protein biosynthesis and gene expression regulation . These f6) time, where N denotes the length of the compared sequences ’ for dot-bracket structures in combination with RNA sequence (20 combinations) to represent each of the RNA nucleotides and the secondary structure assigned to it ,arbitrarily assign one of the letters from the protein alphabet to structural elements from dotbracket notation without combining it with RNA sequence.As described above, there are two ways to utilize 20 characters of the protein alphabet to represent RNA structure:In this way, it is possible to convert secondary structure or secondary structure with RNA sequence to a new sequence that utilizes the protein alphabet—the pseudo-amino acid sequence. This process is fully reversible, therefore the secondary structure (together with RNA sequence in the first case) can be easily obtained from pseudo-amino acid sequence. However, pseudo-amino acid sequences have nothing to do with the protein sequences encoded in mRNA, except for using the same alphabet.Both approaches to the conversion have their drawbacks. In the first case, there are limitations for higher-order pseudoknots—they are treated as unpaired regions to keep proper pairing for remaining base pairs. In the second case, there is no information about RNA sequence that may help prepare better alignment.Details regarding the conversion into all 20 combinations are shown in Additional file It is noteworthy that pseudoknots may be defined in two ways: ((([[[…)))]]] represents exactly the same structure as [[[(((…]]]))). Therefore, we introduced an additional tool that uniformly converts such structures into one common notation.After the conversion of RNA sequences to pseudo-amino acids, the running of a multiple sequence alignment program dedicated to protein sequences provides the most adequate structural RNA alignment. The MUSCLE program provides such a function for RNAlign2D, utilizing a scoring matrix dedicated to RNA structural alignment. The default scoring matrix for sequence and structure conversion is shown in Addition file Scoring matrix was automatically generated using a selected set of parameters describing scores for pairs of dot-brackets. Different scores are assigned to the same type of bracket or two dots, opposite brackets, different brackets, brackets and dots. Moreover, there is an additional bonus for the same sequence in the aligned molecules. In total, there are eight parameters, including gap opening and gap extension penalty. Theoretically, it is possible to introduce more parameters or even to treat each entry in the matrix separately, but it will most likely lead to overfitting, as there are not enough aligned sequences that can be used to calculate the scoring matrix in this way. To perform an optimal alignment, every parameter of the scoring matrix was optimized using BraliBase 2.1  k7 datasRNAlign2D is a command line tool written as a Python3 script that works in UNIX-based operating systems. It is installed via python3-setuptools. Furthermore, MUSCLE aligner requires separate installation. RNAlign2D was tested with MUSCLE v3.8.31. RNAlign2D performs the following processing steps , or to standardize pseudoknot notations. Additionally, the script ‘create_matrix.py’ allows the user to define a customized scoring matrix and calculate_consensus.py to calculate consensus structure for a given alignment. The ‘pseudo’ mode is experimental feature for higher order pseudoknots, where sequence is not taken into account and it should be used sparingly.RNAlign2D can be run by simply writing the following command in a terminal: The input file used to run RNAlign2D in both ‘simple’ and ‘pseudo’ mode is a FASTA-like file including a header followed by a line containing the sequence and 2D structure in a dot-bracket format. In the ‘pseudo’ mode, the sequence line in this file is omitted during conversion and alignment. When structures with higher-order pseudoknots are analyzed in the ‘simple’ mode, the residues in higher-order pseudoknots are treated as unpaired residues to ensure proper pairing of remaining residues. Moreover, RNAlign2D ‘normalizes’ structures to ensure that pseudoknots are written in a uniform way.RNAlign2D was compared with LocARNA, CARNA, MAFFT, TurboFold II, and STRAL, using BraliBase 2.1  and dataFor alignment of the BraliBase 2.1 benchmark dataset, RNAlign2D, LocARNA, and CARNA generated similar mean SPSs and PPVs for all datasets, which ranged from 0.89 to 0.93 , ≥ 3 (k5), and ≥ 5 (k10) common sequences with the k7 dataset for RNAlign2D. We observed only minor, non-significant changes, which means that our scoring matrix is not over-fitted.To check the performance of alignment of RNA sequences from specific RNA families, we used the RNAStralign benchmark dataset . When thThe SPSs, PPVs, and standard deviations from the alignment of all datasets with all the alignment tools tested are summarized in Additional file As expected, programs that utilize known RNA structures produce better structural alignments than those that predict 2D structures. For the BraliBase2.1 benchmark, RNAlign2D, LocARNA, and CARNA have similar, very low mean structural distances, while for STRAL and TurboFold II these distances are much higher Fig. . A similAlignment times from each of the analyzed groups of RNAs from the RNAStralign benchmark datasets were determined and compared. RNAlign2D was the fastest tool for the alignment of datasets containing 20 and 10 molecules Fig. , with thRNAlign2D is an extremely fast RNA alignment tool and thus allows the alignment of hundreds of RNA molecules in a very short time. It mediates alignment of RNA molecules with known 2D structures, where 2D structure is required as part of the input. RNAlign2D contains an option to model missing structures by using RNAfold from the ViennaRNA package , but in Overall, the RNAlign2D alignment performance (as indicated by SPSs and PPVs) is similar to LocARNA, CARNA, and TurboFold II, but RNAlign2D aligned the RNA sequences several hundred times faster than those tools. In some cases (e.g. RNase P and telomerase), it produced better alignment. In comparison to MAFFT and STRAL, RNAlign2D produced better alignment in the majority of benchmark datasets. However, alignment accuracy was strongly dependent on the RNA family and the different average pairwise sequence identity (APSI) values of the aligned sequences. Based on our benchmark results, RNAlign2D can be recommended as a first-choice tool for the alignment of large numbers of sequences with an APSI ≥ 50%. For instance, it can be used to align all members of a particular RNA family or all known tRNA isoacceptors/isodecoders for a specific amino acid. Results of such alignments can be further utilized to perform and/or improve 3D structure modeling.For sequences with a low APSI , the performance of alignment with RNAlign2D was worse than that with LocARNA, CARNA, TurboFold II and MAFFT. It can be expected that a scoring matrix optimized for multiple RNA families could be sub-optimal for at least some of these families, including SRP in this case. We observed that in comparison to the SRP reference alignments, RNAlign2D introduced in general fewer gaps, especially in the stem regions and single-nucleotide bulges. Additionally, the introduced gaps are usually longer. This issue can be solved by changing the parameters in the scoring matrix, decreasing gap-opening penalty, or creating a scoring matrix optimized for the particular RNA family.In terms of structural alignment quality, measured as mean structural distance between consensus structure and all structures in the input, RNAlign2D outperforms tools that use RNA structure prediction (STRAL and TurboFold II), which was expected. In comparison to other tools that utilize known RNA structure (LocARNA and CARNA), our tool was worse in the cases of telomerase and SRP, and at a very similar level for other datasets. It is worth noting here that better sequence alignment does not always mean smaller structural distance (as for the telomerase dataset).We believe that there is still field for improvement of our approach in the future. To perform the best benchmark possible, we decided to use most of the available alignments for benchmark purposes. Therefore our training set was very limited. In case of the more manually curated structural alignments were available, it might be possible to introduce machine learning methods for optimization of either parameters specified in this publication or even each of the scoring matrix parameters.In conclusion, RNAlign2D uses a novel approach to align RNAs with known 2D structures, and with the growing number of experimentally determined RNA 2D structures, this approach will be further improved by optimization of scoring matrices for the particular RNA families and/or utilizing different aligners. It offers a reliable compromise between the computationally demanding approaches and fast, but much less accurate ones.For benchmark purposes, RNAlign2D was compared with LocARNA (version 1.9.2.3)  and CARNThe benchmark on RNAStralign dataset was made as described by Tan et al. . Namely,In the case of 16S rRNA sequences from Alphaproteobacteria, we observed differences between some sequences in the ct files used as a test set and fasta file with reference alignment. Therefore, we first removed the sequences that differed from both the test and reference sets  and then proceeded to selection and analysis.Sequences from the protozoan SRP reference alignment file contain a considerably higher number of unknown bases (Ns) than the same sequences in the test dataset used to perform alignments. Therefore, we utilized a custom Python script to replace unknown bases in the reference sequences based on the test dataset sequences and then employed these corrected reference sequences to calculate alignment accuracy.We ran LocARNA, CARNA, STRAL, TurboFold II, and RNAlign2D (‘simple’ mode) with the following default parameters to align the complete benchmark datasets: #locARNA, mlocarna $file.raw.fa; #CARNA, mlocarna –pw-aligner carna $file.raw.fa; #STRAL,./stral $file.fa; TurboFold II,./TurboFold $file.config.txt ; #RNAlign2D, rnalign2d -i $file.raw.fa -o $file.raw.fa.out. MAFFT was used in mxscarna mode, to predict RNA 2D structure #./mafft_mxscarnamode $file.fa.In the next step, SPSs and PPVs were calculated for each alignment. The output files of LocARNA and CARNA are in ClustalW aln format. To perform the calculations, we converted these files to FASTA format using the fasconvert tool from the FAST package (version 1.06) . The outTo compare the mean SPSs and PPVs from RNAlign2D and other benchmarked programs, we applied the two-sided t-test, because of its better performance in comparison to non-parametric statistical test for large sample sizes, also when analyzed data are not normally distributed , 31.To compare structural alignment accuracy between benchmarked programs, we calculated a mean from structural distances between consensus structure from each alignment and every single structure taken as input to the alignment, using RNAdistance  from ViennaRNA package . ConsensTo determine the time required to perform each alignment, we used 40 groups of 5, 10 or 20 sequence homologs from the RNAStralign benchmark dataset. The LocARNA, CARNA, TurboFold II, MAFFT, STRAL, and RNAlign2D running times for each group were measured using the bash ‘time’ command.Project name: RNAlign2D.https://github.com/tomaszwozniakihg/rnalign2dProject home page: Operating system(s): Linux, Mac OSX.Programming language: Python 3.Other requirements: MUSCLE (tested on version 3.8.31), pytest (tested on version 5.1.3), Vienna RNA .License: MIT.Any restrictions to use by non-academics: no.Additional file 1. Figure S1. (A) Structure conversion to a pseudo-amino acid sequence for RNA with higher-level pseudoknots. (B) Conversion of structure elements to pseudo-amino acids and their scores (left) and the default scoring matrix (right).Additional file 2. Table S1. Mean sum-of-pair scores (SPS) and positive predictive values (PPVs) with standard deviations obtained in BraliBase2.1 and RNAStralign benchmarks. In the highlighted fields, values differed between the full BraliBase2.1 benchmark  and a smaller version of benchmark, where datasets containing ≥ 2 , ≥ 3 (k5), and ≥ 5 (k10) common sequences with k7 dataset were excluded .Additional file 3. Table S2. Running time measurement for RNAlign2D in comparison to other aligners.Additional file 4. Table S3. Bralibase2.1 dataset used to prepare benchmark. Additional sheet contains the numbers of overlapping sequences between the k7 dataset used for scoring matrix optimization and other Bralibase2.1 datasets.


The well-known fact that protein structures are more conserved than their sequences forms the basis of several areas of computational structural biology. Methods based on the structure analysis provide more complete information on residue conservation in evolutionary processes. This is crucial for the determination of evolutionary relationships between proteins and for the identification of recurrent structural patterns present in biomolecules involved in similar functions. However, algorithmic structural alignment is much more difficult than multiple sequence alignment. This study is devoted to the development and applications of DAMA—a novel effective environment capable to compute and analyze multiple structure alignments.QC, which is the ratio of correctly aligned columns, and when applying the methods to more difficult cases.DAMA is based on local structural similarities, using local 3D structure descriptors and thus accounts for nearest-neighbor molecular environments of aligned residues. It is constrained neither by protein topology nor by its global structure. DAMA is an extension of our previous study (DEDAL) which demonstrated the applicability of local descriptors to pairwise alignment problems. Since the multiple alignment problem is NP-complete, an effective heuristic approach has been developed without imposing any artificial constraints. The alignment algorithm searches for the largest, consistent ensemble of similar descriptors. The new method is capable to capture most of the biologically significant similarities present in canonical test sets and is discriminatory enough to prevent the emergence of larger, but meaningless, solutions. Tests performed on the test sets, including protein kinases, demonstrate DAMA’s capability of identifying equivalent residues, which should be very useful in discovering the biological nature of proteins similarity. Performance profiles show the advantage of DAMA over other methods, in particular when using a strict similarity measure http://dworkowa.imdik.pan.pl/EP/DAMA. Linux binaries of the software are available upon request.DAMA is available online at Bioinformatics online. Structures of proteins are conserved more than their sequences. It is believed, that methods based on analysis of structure, rather than sequence analysis, provide more complete information on residue conservation in evolutionary processes. This knowledge is crucial for both, determination of evolutionary relationships between proteins, and for the identification of structural patterns involved in similar functions. Unfortunately aligning structures is a computationally much more demanding task than aligning sequences. Nevertheless, structure alignment is commonly used in protein classification e.g. . One of The MStA problem can be formulated in several ways and existing algorithms differ by the type of alignments they compute. We distinguish methods in which structures are treated as rigid bodies  from methods that allow a certain level of flexibility . Some algorithms may allow for circular permutations (and other rearrangements) (MASS).Most approaches either gradually join pairwise structural alignments into one MStA , or align all structures simultaneously.DAMA has been designed to bridge a gap between these approaches and ensure robust performance while imposing the least constraints on the solution. Quality of alignments is achieved by ensuring that the entire molecular environment of the aligned residues is taken into account. There are no constraints on the global superposition, which enables alignment of structures with significant spatial distortions (e.g. a different arrangement of domains connected by a flexible linker). Circular permutations and segment swaps are allowed as well. The concept of progressive alignment is applied in the initial phase for generating several alignments to be further improved by an evolutionary algorithm. Selected algorithms have already been implemented and optimized for CUDA graphical processors  pairwise alignments .The main difficulty in building a multiple alignment from descriptor alignments results from the fact, that contrary to the pairwise case, it is not enough to select a set of descriptor pairs in which every two of them form a valid alignment. This condition does not prevent conflicts of a kind mentioned above. Therefore, an approach based on computing maximal cliques cannot be straightforwardly used in this case .We have overcome this problem by building an alignment incrementally. If we divide the set of structures into two sets, an optimal multiple alignment is an optimal alignment of multiple alignments of these subsets . Aligning two multiple alignments may lead to conflicts as well, but their number is expected to be low, when similarity within these alignments is high. This principle leads to an application of a neighbor join (NJ) method. We use a randomized version of the NJ algorithm to generate a set of specimens to be further improved by an evolutionary algorithm.dC denote distances between Descriptors have already been applied in several studies . Here, wIt should be noted that this kind of similarity is totally sequence-independent. Because elements are the smallest indivisible blocks, it is possible that one segment will be aligned to two smaller ones which are a few residues apart.Once a set of pairs of similar descriptors is computed, one can define a graph, where nodes correspond to descriptor pairs. It can be easily proven, that the largest alignment of two structures corresponds to a maximal clique in such a graph.Cliques in the graph are identified using a heuristic approach based on the Motzkin-Straus theorem by iteratively searching for a maximum of a certain quadratic form which corresponds to the largest clique in the graph  of the backbone. These distances are averaged for each residue over all its contacts, for each pair of structures, and after raising to the power of two for the whole multiple alignment. The result can be viewed as an average ‘tension’ exerted on structures when superimposed structures are treated as elastic objects.α is an angle between rotations required to superimpose the largest region and the augmented one, respectively.Sometimes a pairwise alignment can be divided into regions with no contacts between them. In such a case, possible conformational distortions would not influence tension. Therefore, we augment the score of all regions, except the largest one, by a factor proportional to We use a two-stage process similar to the one used by our DEDAL method . An optiWe have implemented the described algorithm in C on the Linux platform. For the rapid finding of cliques in large graphs we have used CUDA-MS—a GPU accelerated library  is the ratio of correctly aligned columns, while a more lenient one (QP) is the ratio of correctly aligned residue pairs. It should be noted, that all alignments in SISY-multiple have all columns completely filled . We present values of QC and QP for alignments computed by Caretta . If one disregards cases for which programs have failed, Matt , POSA  and MUSTANG  perform better than 3DCOMB. However, these three methods have the highest number of failures. Results for the remaining methods are provided in DAMA turned out to be the most accurate method on the whole SISY-multiple dataset achieving median accuracy of 82.3% for the m for the alignment a—with either the QC or QP measures. We define accuracy ratio as QC (a) and QP (b) measure are presented in et al. are presented in QC and QP profiles for these methods indicating that DAMA is more likely to align whole columns correctly, and thus identify the whole common core. The numerical values of AUC are provided in Performance profiles  are convSample alignment from the SISY-multiple dataset returned by DAMA (containing circular permutation) can be found in As reference, we have taken an alignment of 31 kinases prepared by Scheeff and Bourne . This al5.2.1There are few residues playing important role in kinase activity which are conserved in all structures, in particular K72, E91, D166 or D184 [positions according to PKA (1cdk)]. Residues crucial for the ATP hydrolysis (D184) and present in the catalytic region (D166) were aligned correctly by both methods. However, only DAMA did equally well with residues responsible for ATP stabilization in the binding pocket (K72 and E91), while 3DCOMB shifted its alignment of three structures by 1–2 residues see . There i5.2.2There are several secondary structures conserved in the reference alignment. Among them, helices denoted with letters A–F are common for all structures (except for B-helix), while G to I ures see .C- and D-helices or The most troublesome elements were helix B (or its substitute loop), and 5.2.3There are four insertions present only in some kinases. One present in the catalytic region of one structure—AFK—was not aligned with any residues from other structures by both programs as expected. Long insertions in CKA-2 and APH were correctly aligned by DAMA, and 3DCOMB achieved approximately 25% accuracy see . The sim5.2.4Summary of all identified structural features and performance of DAMA and 3DCOMB can be found in DAMA is capable of computing multiple alignments of large sets of structures, imposing only minor constraints in the process. It is based on local similarities, which may comprise several disjoint segments of a protein backbone and encompass complete physico-chemical neighborhoods of amino-acid residues. It is constrained neither by protein topology, thus permitting segments swaps and circular permutations, nor by its global structure allowing for conformational variability which, in particular, is essential for enzymatic and other activities.In our approach, the alignment algorithm searches for the largest non-contradictory ensemble of similar descriptors. Local descriptors are generic enough to capture most of the biologically significant similarities present in the test set, while at the same time they are discriminatory enough to prevent the emergence of larger, but meaningless, solutions. This result is consistent with our previous study demonstrating the applicability of local descriptors to the pairwise alignment problem , 2014.When solving a so-called black-box optimization problem, in which an optimized function  cannot be differentiated and has to be independently evaluated for each attempted solution, it is crucial to limit the number of unsuccessful trials by reducing the number of infeasible solutions. It has been established that the multiple alignment problem is NP-complete , and thu3DCOMB, the second-best method after DAMA tested in this study, uses a similar definition of local similarities—so-called local and global structure environments. Its search space is subject to the aforementioned constraints due to the usage of dynamic programming and TM-score  for asseQC measure.The main goal to compute multiple alignments is not only to assess whether given structures are similar, but also to discover the exact nature of this similarity. Such an aim can be achieved only if an alignment algorithm reliably reconstructs whole columns of the optimal alignment. The test performed on protein kinases shows DAMA’s capability in this respect. Performance profiles see  also shoThis study was supported by the Z-526/22 [IMDiK PAN] and PP/BF-501-D111-01-1110102 (UW) funds. Computations were carried out using the infrastructure funded by the POIG.02.03.00-00-003/09 (Biocentrum-Ochota) and POIG.02.01.00-14-122/09 (Physics at the basis of new technologies) projects.Conflict of Interest: none declared.btab571_Supplementary_DataClick here for additional data file.