All molecular functions and biological processes are carried out by groups of proteins that interact with each other. Metaproteomic data continuously generates new proteins whose molecular functions and relations must be discovered. A widely accepted structure to model functional relations between proteins are protein-protein interaction networks (PPIN), and their analysis and alignment has become a key ingredient in the study and prediction of protein-protein interactions, protein function, and evolutionary conserved assembly pathways of protein complexes. Several PPIN aligners have been proposed, but attaining the right balance between network topology and biological information is one of the most difficult and key points in the design of any PPIN alignment algorithm.Motivated by the challenge of well-balanced and efficient algorithms, we have designed and implemented AligNet, a parameter-free pairwise PPIN alignment algorithm aimed at bridging the gap between topologically efficient and biologically meaningful matchings. A comparison of the results obtained with AligNet and with the best aligners shows that AligNet achieves indeed a good balance between topological and biological matching.In this paper we present AligNet, a new pairwise global PPIN aligner that produces biologically meaningful alignments, by achieving a good balance between structural matching and protein function conservation, and more efficient computations than state-of-the-art tools. But since the alignments between regions of the pair of PPIN could be mutually inconsistent, it could be impossible to merge the alignments between regions into an alignment of the whole networks. In contrast, a global alignment algorithm is aimed at finding the best overall alignment between whole PPIN [One of the most difficult problems in systems biology is to discover protein-protein interactions as well as their associated functions. The alignment and analysis of protein-protein interaction networks (PPIN) has become a key ingredient to obtain functional orthologs as well as evolutionary conserved assembly pathways of protein complexes. With this purpose, several pairwise alignment algorithms have been proposed in the last 15 years. The early aligners [ole PPIN . Severalole PPIN , 7–11.Most PPIN aligners are based on the idea that “two nodes are similar when their corresponding neighbors are so,” taking into account both the network topology and the biological features of the proteins in the definition of “similarity.” The problem is that attaining the right balance between network topology and biological information is one of the most difficult and key points in any PPIN alignment algorithm. As it is shown in , 13, whehttps://github.com/biocom-uib/AligNet.Motivated by this lack of well-balanced and efficient algorithms, we have designed AligNet, a parameter-free pairwise PPIN alignment algorithm aimed at filling the gap between efficient topologically and biologically meaningful matchings. The overall idea of the algorithm is to obtain many local alignments that are combined and extended into a meaningful global alignment. The final alignment captures the benefits of considering both types of alignments: with the local alignments we capture the topological similarity between the networks and we speed up the running time of the algorithm, while with the final global alignment we solve the inconsistencies among the local alignments and yield an overall alignment of the pair of input PPIN. AligNet has been implemented in R , and theA comparison of the results obtained with AligNet and with the best aligners assessed in , 13 showgraph we understand an undirected graph, that is, a structure G= with V a finite set of nodes and E a family of 2-element subsets {u,v} of V called the edges of the graph. A PPIN is modelled in a natural way as a graph, with its nodes representing the proteins and its edges, their interactions.In this paper, by a G= be a graph. We say that an edge e={u,v} is incident to u and v. The nodes v such that {u,v}∈E are the neighbors of u, and they form the set NG(u). The degree deg(u) of a node u∈V is the number of edges incident to it. A path between two nodes u,v∈V is a sequence of pairwise different edges {u,u1},{u1,u2},…,{uk−1,uk},{uk,v} such that the first and last edges are incident to u and v, respectively, and every pair of consecutive edges share a node . The length of a path is the number of edges forming it, and its intermediate nodes are u1,…,uk. Two nodes are connected when there exists a path between them. For every pair of connected nodes u,v∈V, their distancedG in G is the length of a shortest path connecting them. The diameterD(G) of G is the maximum distance between any two connected nodes in G. The cardinality of a set X is denoted by |X|.We introduce now some notations. Let G= and G′= representing two PPIN  and it produces, as output, a similarity score for them and a local and a global alignment between them. Figure C(G) and C(G′), respectively, of the input networks G and G′.The computation of overlapping clusterings C(G) and C(G′).The computation of alignments between pairs of clusters in C(G) and C(G′).The computation of a matching between G and G′.The computation of a local alignment of the input networks The extension of this local alignment to a meaningful global alignment.AligNet receives as input two graphs Step 1. Overlapping clusterings. The first step in AligNet consists in computing an overlapping clustering of each input network. These clusterings are based on the following similarity score s between pairs of proteins (nodes) u,v in a PPIN G: If u,v are not connected by a path, then s=0, and if they are connected,B is the normalized bit score of the proteins u and v, that is, the rescaled version of their alignment score obtained with BLAST+, which is independent of the size of the search space [ch space . The intα be the third quartile of the distribution of the similarity score values of pairs of nodes, so that only 25% of the pairs of nodes  are such that s>α. Then, for every node u∈V, the clusterCu in Gcentered at u isC(G)={Cu∣u∈V} and To obtain the overlapping clustering of an input network, we define a cluster centered at each node. To avoid the choice of a fixed and arbitrary cluster size, we considered the similarity score distribution and define the cluster centered at each node as follows. Let Figure Step 2. Alignments between pairs of clusters. In this second step, AligNet computes an alignment between every pair of clusters Cu∈C(G) and B>0. These alignments define an alignment score between every such a pair of clusters that will be used in the third step to compute a matching between C(G) and C(G′).u∈V and u′∈V′ such that B>0, the alignment between Cu∈C(G) and u with u′. Set Match v∈Cu∩NG(u) and for every F value, and concatenate them to For every lgorithm . Sort thu,u′) by the rest of the pairs in Cu and Iterate step (ii), replacing  of F. Then, compute a matchingF value, and concatenate them to More specifically, in the Formally, for every Cu that are matched to nodes in u is matched to u′ and then a node v∈Cu should be matched to a node v′ in u with v and u′ with v′ such that their intermediate nodes are already aligned in sequential order along the paths. The alignment procedure gives priority to matching neighbors of nodes x,x′ at the possible shortest distance of the respective cluster centers and with F as large as possible among those pairs already matched at the same iterative step.The resulting alignment Step 3. Matching between families of clusters. Letscore of each This score assesses simultaneously the average similarity of the sequences of the proteins matched by C(G) and C(G′) by applying the maximum weighted bipartite matching algorithm to the bipartite graph whose nodes are the clusters in C(G) and C(G′), whose edges connect pairs of clusters Cu∈C(G) and B>0, and the weight of the edge connecting Cu with Once computed all these scores, AligNet obtains a matching between Step 4. Local alignment of PPIN. In this step, AligNet produces a local alignment between G and G′ from the matching between C(G) and C(G′) obtained in the previous step.V⊔V′ and where every mapping The main idea is to define this alignment by merging the partial injective mappings best-scored alignments built recursively as follows. Starting with w0 not belonging to the union of the domains of the mappings However, in order to decrease the computation time of AligNet, we do not define this hypergraph from the whole V⊔V′ and hyperarcs defined by the mappings G and G′ as a solution of the corresponding weighted bipartite hypergraph assignment problem [Then, Alignet obtains from the directed hypergraph with nodes  problem . Figure Step 5. Global meaningful alignment of PPIN. In order to extend the local alignment produced in the previous step, AligNet iterates the following procedure:G and G′ that have already been aligned, and it recomputes the score of each alignment It removes the nodes in C(G) and C(G′), as in step 3, but using as edges those It computes a new optimal matching It computes a new set V∪V′ not yet aligned and hyperarcs the mappings It defines a new directed hypergraph whose nodes are the nodes in V and V′ by solving the weighted bipartite hypergraph assignment problem for this hypergraph, and it adds this local alignment to the alignment obtained so far.It computes a local alignment between unaligned nodes in This procedure is iterated while there exist nodes not aligned belonging to the domain or the image of some alignment M. musculus (mus), C. elegans (cel), D. melanogaster (dme), S. cerevisiae (sce), and H. sapiens (hsa), downloaded from the IsoBase database [In this section we report the tests performed to assess the performance of AligNet. Following the comparisons published in , 13, we database  (versionedge correctness ratio (EC), which quantifies the amount of structure preserved by the alignment, and the functional coherence value (FC), which assesses the functional similarity of the aligned proteins by comparing their Gene Ontology annotation. More formally, let G= and G′= be two PPIN such that |V|≤|V′| and let μ:V→V′ be a mapping defining an alignment. The edge correctness ratio of μ isfunctional coherence value of μ isFS is defined byGO(u) and GO(u′) the sets of GO annotations of the proteins u and u′, respectively.In a first assessment of the alignments, we used two quality measures: the FCmax of the FC score for every pair of networks. This maximum value FCmax was obtained solving the maximum weighted bipartite matching problem, where the complete bipartite graph had the proteins as nodes and the weight of each edge connecting one protein in a network to a protein in the other network was the FC score of the corresponding pair of proteins. These maximum values are listed in Table Tables noise to the networks by randomly adding and deleting 5% of the edges. For every aligner, we were able to compute 100 new pairwise alignments considering the perturbed networks of M. musculus mapped to the perturbed networks of C. elegans, D. melanogaster, and S. cerevisiae. In this way, for every aligner we ended up with a sample of 100 EC and FC scores for each of the alignments mus–cel, mus–sce and mus–dme. In Table In addition, in our first test and in order to measure the amount of variation or dispersion of the EC and FC scores used to evaluate the aligners, we introduced some complex functional coherence value (CFC), defined as the ratio of complexes that are aligned correctly with respect to the aligned complexes. More specifically, if we call a pair of complexes, one in each network, coherent when they share some biological function and incoherent otherwise, and if we denote by CP and NCP the numbers of coherent and incoherent pairs of aligned complexes, then CFC value (25.34) followed by PINALOG (24.48) whereas HubAlign and L- GRAAL obtained a very low CFC value .As a second test, we compared the behavior of AligNet, PINALOG, HubAlign, and L-GRAAL in relation to the alignment of protein complexes (we excluded SPINAL from this test because its results in the EC and FC tests were not convincing). Following the procedure explained in , we consA, the complexes that were not aligned either by AligNet or by A; the coherent and incoherent pairs among those complexes that were aligned by AligNet but not by A; and the coherent and incoherent pairs among those complexes that were aligned by A but not by AligNet. The results are given in Table In order to further compare the results obtained by AligNet on protein complexes with those of the others aligners, we counted, for each other aligner S. cerevisiae and H. sapiens, a true possitive (TP) is an essential protein matched to an essential protein while a false possitive (FP) is an essential protein matched to a non essential one. In the same way, a true negative (TN) is a non essential protein matched to a non essential one and a false negative (FN) is a non essential protein matched to an essential one. The essential proteins information was retrieved from the DEG Database [specificity defined by TN/N, precision defined by TN/N, F1-score defined by 2TP/(2TP+FP+FN), accuracy defined by (TP+TN)/(P+N) and balanced accuracy, defined by ((TP/P)+(TN/N))/2, where P and N are the number of essential and non essential proteins respectively in S. cerevisiae. Also, we calculated the Pearson correlation of this binary classification problem, called MCC (Matthews Correlation Coefficient) defined byproficiency, also called uncertainty coefficient or entropy coefficient. The uncertainty coefficient in this test is defined as follows: let {p1,…pn} be the set of proteins in S. cerevisiae and let η be an alignment between the two PPIN S. cerevisiae and H. sapiens. Two random variables X and Y are considered such that, X is a binary vector X=(xi)i=1,…,n such that xi takes the value 1 if protein pi is essential and the value 0 otherwise. Y is a binary vector Y=(yi)i=1,…,n such that yi takes the value 1 if protein η(pi) is essential and the value 0 otherwise. Then, the uncertainty coefficient is defined byH(X) is the entropy of X and H(X|Y) is the conditional entropy. In this test, the uncertainty coefficient measures the capability to predict that a S. cerevisiae protein is essential provided that its image by η is essential. In Fig. F1-score, HubAlign obtained the lowest value while the others aligners are comparable. The highest proficiency and MCC values were obtained by AligNet while the lowest one was obtained by PINALOG.As a third test to evaluate the aligners, we considered the essential proteins, i.e. those proteins that are indispensable for the survival of an organism, again in the human and yeast PPINs. We evaluate the aligners performance assuming that essential proteins must be aligned to essential proteins. Thus, for every alignment between the PPIN of Database  , SPINAL , HubAlign (downloaded from" https://github.com/hashemifar/HubAlign) and also L-GRAAL (downloaded from http://www0.cs.ucl.ac.uk/staff/natasa/L-GRAAL/). NATALIE could not align the two smallest networks, C. elegans and D. melanogaster, on a computer with 64 GB of RAM. PINALOG, SPINAL, HubAlign and L-GRAAL were able to complete all the alignments. In order to visualize their running times, we show the running time of every finished computation for each aligner in the top barplot in Fig. S. cerevisiae and H. sapiens. Thus, in order to visualize the results in the cases where the aligners consumed less than 3,500 seconds, we describe in Fig. Finally, in order to study the efficiency of the considered aligners, we observed their running time and memory space needed to perform an alignment. We run our implementation of AligNet on a server with 4 processors at 2.6 GHz and 20 GB of RAM and we also run the latest implementations of PINALOG  to measure the quality of an alignment in terms of its behaviour on protein complexes. The CFC score is defined as the ratio of complexes that are aligned correctly with respect to the aligned complexes. In Table CFC value (25.34) followed by PINALOG (24.48) and HubAlign but L- GRAAL obtained a very low CFC value . In order to further compare the results obtained by AligNet on protein complexes with those of the others aligners, we counted, for each other aligner A, the complexes that were not aligned either by AligNet or by A; the coherent and incoherent pairs among those complexes that were aligned by AligNet but not by A; and the coherent and incoherent pairs among those complexes that were aligned by A but not by AligNet. The results are given in Table In the second test we evaluated the alignment of protein complexes. We used the F1-score, HubAlign obtained the lowest value while the others aligners are comparable. The highest proficiency and MCC values were obtained by AligNet while the lowest one was obtained by PINALOG.In the third test we evaluated the alignment of essential proteins in the human and yeast PPINs. We evaluated the aligners performance assuming that essential proteins must be aligned to essential proteins and we compute seven statistical measures. In Fig. C. elegans and D. melanogaster, on a computer with 64 GB of RAM. With respect to PINALOG, SPINAL, HubAlign and L-GRAAL, we were able to complete all the alignments. In order to visualize their running times, we show the running time of every finished computation for each aligner in Fig. H. sapiens and S. cerevisiae, and also between D. melanogaster and S. cerevisiae. PINALOG is the slowest one, also with a big difference, to compute the alignment between C. elegans and H. sapiens, as well as the alignment between H. sapiens and M. musculus. AligNet is considerably faster than PINALOG and SPINAL, with a running time of less than 1,000 seconds in most of the alignments. Only in one computation, the alignment between D. melanogaster and H. sapiens, AligNet is slower than PINALOG and SPINAL, and the difference is less than 2,000 seconds. Because SPINAL needed more than 20,000 seconds for the alignment between S. cerevisiae and H. sapiens, it is difficult to see the running times in some alignments. Thus, in order to visualize the results in the cases where the aligners consumed less than 3,500 seconds, we show in Fig. Finally, one of the weak points of PPIN aligners is their lack of efficiency. Indeed, as we have already mentioned, although NATALIE was suggested as a good aligner, it could not align the two smallest networks, We also present the running times ordered by the networks size in Fig. https://github.com/biocom-uib/AligNet.In this paper we present AligNet, a new method and software tool for the pairwise global alignment of PPIN aimed to produce biologically meaningful alignments by achieving a good balance between structural matching and protein function conservation. AligNet is a parameter-free algorithm that, given two PPIN, produces a consistent alignment from the smaller network, in terms of number of nodes, to the larger network. Its implementation in R is freely available from In order to assess the correctness of AligNet, we have evaluated the quality of the alignments obtained with it and with the 4 best aligners established in , 13, nam


Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and  In other words, a known similar protein is used to build a new atomistic 3D structure.The protein folding problem has become an integral part of modern biology; with a historical tale that began nearly over half a century ago. Proteins are diverse heterogeneous polymers comprised of gene-coded primary sequences of amino acid monomers. Pioneering work on identification of hydrogen bond-linked protein secondary structures like α-helix by Linus Pauling and others in the 1950s paved the way to accurate experimental elucidation of atomistic (ab initio modeling (FM category or free modeling), fold recognition and others. It was unavoidable that this experiment will shift from individual to collective intelligence (CI). Teams started to share their experiences and sooner or later what used to be biennial top-secret projects quickly became a catalyst for collaboration and development in all teams over the years. CASP and other CI initiatives will be discussed briefly in this review, covering the historic aspects of homology modeling and the lessons learned in recent years . ML is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. According to recent definitions, it is an umbrella term that refers to a broad range of algorithms that perform intelligent predictions based on a dataset 2et al. (2000) e.g. catalytic enzymes and their substrates), the structural roles of proteins in the cell (some proteins serve as building blocks in the cell), and protein interactions (such as antibody binding) e.g. entire virus 3D structure), and in protein–protein interaction studies In one seminal review, Marti-Renom 1.Identification and selection of templates (other homologous proteins with known 3D structures). Depending on the first principle, we start searching for eligible templates based on sequence–sequence alignment, while narrowing our search to the crystal structures deposited at the Worldwide Protein Data Bank (wwPDB) database (http://www.wwpdb.org/). The eligible templates are chosen using protein Basic Local Alignment Search Tool (BLASTp). In the case of low homology , alternative methods are used for alignment to reduce shifts and gaps such as profile-profile alignments, Hidden Markov Models (HMMs) and position-specific iterated BLAST (psi-BLAST). Profile HMMs generate more accurate alignments than psi-BLAST, such as HMM-HMM–based lightning-fast iterative sequence search  e.g. CLUSTALW 2.correction and optimization of the chosen alignments  in order to build the whole backbone the previous step is followed by 3.The 3-D model building is then performed using one of four different approaches i.The rigid-body assembly method collects rigid body parts together, which are picked up from the aligned template protein structures, using programs like 3D-JIGSAW, BUILDER, and SWISS-MODEL.ii.The segmented matching method relies on comparing the template and structures in the database, based on the sequence identity, geometry, and energy such as the SegMod/ENCAD program.iii.The spatial restraint method approach is another method that depends on the restrains of the template, and can be done using MODELLER.iv.The artificial evolution method depends on rigid-body assembly and stepwise template evolutionary mutations, and can be done using NEST.4.loop modeling. Loops sometimes contribute to important protein functions where the accuracy of loop prediction is crucial to the model whole value. Loop prediction is a complex process because loops are variable and not conserved. Loop prediction is done by two methods: the first is database search approaches which depend on comparison with all the known proteins, the second is conformational search approach (ab initio) which depends on scoring function optimization; a more direct approach The next step is 5.The addition of side-chains onto the major backbone is very critical step. This process requires selection of a rotamer library, a scoring function and a scanning method 6.model optimization, which is used for increasing the quality of the final model. This step is done by using energy minimization utilizing molecular mechanics force fields, to reduce atomic clashes, and exclude all major and small errors. Further optimization can be done using molecular dynamics and Monte Carlo simulations The previous step is followed by 7.model evaluation and validation  or Verify3D (https://servicesn.mbi.ucla.edu/Verify3D/) can be employed. The value of the model is decided depending on the stereochemistry, physical parameters, knowledge-based parameters, statistical mechanics, and many other criteria. The ultimate model validation would be assessment against real experimental 3D structure. It is advised to use several evaluation methods simultaneously to yield the best results. One of the challenges in modeling is the reduced accuracy or production of incorrect models. Alignment errors are still the main cause of deviations and the previous challenges need careful manual inspection and adjustment even when using fully automated programs The final step is lidation , where tThe process of homology modeling itself is run by seven classical steps :1.Identi3Various molecular graphics programs are used for visualization and editing of protein 3D structures . The numMODELLER SWISS-MODEL ab initio methods. Models are generated using a modified replica-exchange Monte Carlo simulation and the top cluster is selected using SPICKER tool, and the model is finally optimized and evaluated.I-TASSER Phyre de novo structure prediction algorithm, yet it is used for protein folding in divergent domains of homology models. Initial protein folding of short segments is chosen from the protein 3D structure database, whereas longer segments are constructed using 3 and 9-residue fragments selected from the database and combined using the Rosetta algorithm.Rosetta RaptorX ab initio loop modeling. The model is globally optimized by conformational space annealing in which a maximum of three unreliable local regions are reconstructed.GALAXY, GalaxyTBM ab initio modeling principles. Here, co-evolutionary analysis is used for matching amino acid sequence co-variation with physical contact in protein 3D structure, and later, these maps are studied using deep neural networks to identify patterns in protein sequence and co-evolutionary couplings and convert them into contact maps. The approach can be considered a modification on RaptorX modeling. RaptorX uses multiple sequence alignments to predict probabilities of discrete distances (mean and variance) to limit the atom–atom distances in predicted ranges that are used to feed geometric constraint satisfaction algorithm. Unlike RaptorX, AlphaFold exploits the entire probability distribution in a continuous function, which is later minimized.AlphaFold 4et al. e.g. ClustalW), sequence-to-profile alignments , Multiple-sequence alignments , profile-to-profile alignments (e.g. HMAP) and structure-based alignments (via SKA). For identities>30%, their findings showed that profile-to-profile alignments produced the best homology models. Despite their thoroughness and comprehensiveness, the field of homology modeling is rapidly growing beyond a single evaluation experiment. Experimental 3D structure repositories are growing, while homology modeling programs and servers are updated continuously.Due to growing number of programs and tools used in homology modeling, several research groups attempted to benchmark and evaluate the homology modeling programs independently. In a benchmarking experiment, Wallner and Elofsson 5et al. et al. The Wikipedia defines CI as “shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making” 6e.g. MODELLER, SWISS-MODEL, I-TASSER, etc.) established huge database repositories of homology-modeled 3D structures and supplemented them with prediction algorithms for annotation of secondary structures, protein domains and functions. No one can deny the role of CASP in motivating the scientific community for active development of the homology modeling field, although more in some years than others as detailed in the next section. CASP inadvertently contributed to the development of homology model evaluation techniques. It is also clear that developments in ab initio modeling (FM category in CASP) have also inspired the development of homology modeling indirectly through initial contact maps and directly through ab initio loop modeling tools, which are integral part of many homology modeling programs. We have used the term “collective intelligence” to describe the situation when a group of researchers is working, dependently or independently, competitively or uncompetitively, actively or passively, towards a unified goal. Here, we will describe three other examples of CI that shaped our perception of the protein folding problem (not only in homology modeling), namely: @home projects, RosettaCommons and Foldit.Numerous developers of popular homology modeling programs/servers (https://foldingathome.org/) is a distributed computing project that utilizes the volunteers’ computational resources such as CPU power, disk space, and network bandwidth. The project, which started in 2000, used molecular simulations to study the folding and functions of many proteins, in some cases for over 1.5 ms time scale, and published nearly 225 articles. It is estimated that nearly 4 million personal computers around the world are participating in this project and competing to earn points. In their perspective entitled “Screen Savers of the World Unite!”, Shirts and Pande https://boinc.bakerlab.org/rosetta/) is another distributed computing project from David Baker’s lab that was announced in 2005, and currently holds over 53,000 active volunteers from 150 countries. Predictor@home is an example of another distributed computing project to predict 3D structures using dTASSER. However, it was discontinued in 2009 @Home projects are distributed computing projects that motivate volunteers by giving them certified Berkeley Open Infrastructure for Network Computing (BOINC) credits. There are nearly 40 BOINC projects at the moment. Folding@Home (RosettaCommons (founded in 2001) is an example of a collaborative initiative of > 500 developers that began in the mid-1990 s, where the scientific community is sharing a codebase for development of computational algorithms http://fold.it/), also developed in 2008 by David Baker’s lab at the university of Washington de novo protein design (viz. synthetic biology), which were validated by the study of the designed structures using NMR and cryo-EM Another unique initiative is the Foldit project  ab initio models, the refinement methods using molecular dynamics for estimating the accuracy of models, and finally modeling of non-principal templates regions At the end of the second CASP decade, more difficult targets were introduced, while the development of multiple template methods and small single-domain ab initio modeling due to these developed methods. The newly available data for protein sequences and 3D structures from many resources contributed to these previous improvements. The torrents of data have assisted modeling by combining experimental and computational forces together. Many research teams relied on refinement using molecular dynamics. This CASP and the previous one established a new assessment to decide whether a model is adequate for answering a particular biological question. A new assessment category for protein assembly was also added CASP12 edition revealed acceleration in the progress of contact accuracy using new methods for predicting residue-residue contacts, as well as ab initio and homology models due to progress in resolution of cryo-EM. A striking development was seen in backbone accuracy as a result of the effective deployment of deep ML methods. The most surprising improvement was spotted in ab initio modeling, one of the toughest aspects of the experiment, as a reflection of the progress in contact prediction. Homology modeling displayed superior results from CASP12 due to the impact of deep ML methods and contact prediction. Overall, the most two general features that made this total difference are the introduction of a new formulation of contact prediction and new deep network architecture By far, the CASP13 had the most dramatic changes, starting with the changes in target composition from 8Since the mid-1990 s, different computational algorithms were involved in protein secondary and tertiary structure prediction such as genetic algorithms, graph theory, ML and neural networks A) in protein structure prediction were recently reviewed by Torrisi et al. e.g. AlphaFold), multi-class contact maps  in a restricted region just like in a biological system when cortical neurons respond to stimuli only in a restricted region of the visual field (A).CNN are widely used in image analysis and most widely used in protein 3D structure prediction al field A.2.B) are widely used in sequence data such as text and time series, and they learn in a sequential (i.e. autoregressive) way. Therefore, their best application would be for protein sequence generation or prediction of the next amino acid at the terminus of a protein.Recurrent neural networks RNN; B are wid3.C) is an example of unsupervised learning method. Unlike the previous neural networks, VAE does not predict a new output, but rather learns new and simpler representation (map) of the original input through an optimization method called variational inference. VAE can take the protein 3D structure and learn certain properties . This map is correlated with protein 3D structure properties. Convolutional VAE was previously used for clustering of protein folds from molecular simulations Variational auto-encoder VAE; C is an e4.D) is a gaming method between two adversaries: a generator and a discriminator. The former generates a map of a distribution input (e.g. Gaussian), while the latter tries to learn if it is real or fake. The process of learning by the two adversaries continues by stochastic optimization until an equilibrium is reached. This strategy have been successfully applied in loop modeling Generative adversarial network GAN; D is a gaDeep learning convolutional neural networks CNN; A in protIt is important to emphasize that contact maps often contain transitive noise coming from indirect correlations between residues 99.1The world of protein folding has one more mysterious – albeit unfolded – tale that is yet to be told (or fold!). IDPs and the intrinsically disordered protein regions (IDPRs) encompass a vague area of protein science with different rules and possibly unique functions. IDPs and IDPRs are commonly present in all living organisms, with a number that is proportional to the complexity of the organism. Nowadays, numbers are speaking about over 1150 IDPs that possess their own folding rules in terms of conventional biophysical concepts, which are thought to constitute the understanding of the world of protein 3D structure, thus breaking the “structure–function” and “lock and key” paradigms e.g., IsUnstruct tool) e.g., SPINE-D tool) meta-approaches  meta-approaches is a template-based method that depends on homologous known-structure proteins (e.g. GSmetaDisorder3D) In order to tackle this difficult paradox, state-of-the-art tools are used to decipher structural complexity. The multidimensional NMR can be combined with small-angle X-ray scattering (SAXS), and then processed using advanced computational data integration via molecular dynamics simulations The scientific community has recently committed to answering different questions regarding IDPs collaboratively. The critical assessment of protein intrinsic disorder prediction (CAID) is the first fully blind assessment of IDPs predictors, which was obviously inspired by the CASP achievements. CAID addressed two major points in its first edition: Firstly, providing a clear definition of IDPs, and secondly, developing concise strategies to evaluate the performance of prediction methods. Knowing that several editions of the CASP experiment have attempted to tackle the same problems without sufficient results, the CAID was more focused and specialized in this area. CAID experiment has shifted the efforts in providing promising data and in improving the definitions of the boundaries of disordered binding regions 9.2ab initio domain assembly  Nearly 75% of proteins consist of multiple domains (average 2.1 domains in eukaryotes and 1.5 domains in prokaryotes) which are independent structural and evolutionary units that are often reshuffled in genomic rearrangements to form quaternary structures 10To this day, the deterministic search algorithms mentioned in the introduction have worked hand-in-hand with experimental methods. Preliminary information from spectroscopic methods, crystallography and even limited number of atomic contacts in NMR experiments are used these days to optimize deterministic algorithms of molecular dynamics and homology modeling to produce cheaper yet more accurate folding predictions. It is known that excluding large sub-spaces of c-space allows for the detailed scan of larger sized molecules ab initio loop modeling, by employing backbone-dependent rotamer libraries for side-chains addition, and finally by developing deep learning contact maps. The evaluation procedure itself evolved over time. It is very hard to predict what the next layer of the onion would be or what deep impact it can lay upon the field. However, we can at least warn that currently used programs should be written in a format that accepts new additions at any step of the homology process. In this review, we have neglected the developments in computer hardware, which can make costly computations feasible in the near future. There has been several promising strategies that may enhance 3D structure predictions, such as chi1 angle prediction, new structural annotations, solvent accessibility studies, molecular simulations, and hybrid ab initio-homology modeling methods.The lesson learned from deep learning of contact maps in recent years highlights the need for adding more layers of information and processing in future trends in homology modeling. Taking the analogy of onion layers, homology modeling prediction accuracy has advanced each decade through adding new layers of information and processing. This has been carried out by introducing multiple templates and information of secondary structure predictions, then by optimizing Even by using the same datasets, the process of deep learning will continue to improve. One very optimistic recent view sees that “by the end of this century, it is expected that computers will have the power to train neural networks with as many neurons as the human brain” In conclusion, it is evident that the gradual and recent integration of CI and AI has played a significant role in the development of homology modeling accuracy. CI contributed to the development of evaluation methods and the addition of new steps in homology modeling. AI contributed to the data processing and prediction efficiency through contact maps ML. If we perceived the homology modeling as a process of consequent steps executed by independent modules, it is very clear that the accuracy of the homology models was enhanced by introducing new modules, and also by improving current modules. It is hoped that such specialization in modeling tools development will make it possible to customize and test combinations of modules in the future. Here, CI and AI will play great role in integration of different resources for more efficient modeling.The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.Tareq Hameduh: Writing - original draft. Yazan Haddad: Conceptualization, Writing - original draft. Vojtech Adam: Supervision, Funding acquisition, Writing - review & editing. Zbynek Heger: Supervision, Conceptualization, Writing - review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Many complex genomic rearrangements arise through template switch errors, which occur in DNA replication when there is a transient polymerase switch to an alternate template nearby in three-dimensional space. While typically investigated at kilobase-to-megabase scales, the genomic and evolutionary consequences of this mutational process are not well characterised at smaller scales, where they are often interpreted as clusters of independent substitutions, insertions and deletions. Here we present an improved statistical approach using pair hidden Markov models, and use it to detect and describe short-range template switches underlying clusters of mutations in the multi-way alignment of hominid genomes. Using robust statistics derived from evolutionary genomic simulations, we show that template switch events have been widespread in the evolution of the great apes’ genomes and provide a parsimonious explanation for the presence of many complex mutation clusters in their phylogenetic context. Larger-scale mechanisms of genome rearrangement are typically associated with structural features around breakpoints, and accordingly we show that atypical patterns of secondary structure formation and DNA bending are present at the initial template switch loci. Our methods improve on previous non-probabilistic approaches for computational detection of template switch mutations, allowing the statistical significance of events to be assessed. By specifying realistic evolutionary parameters based on the genomes and taxa involved, our methods can be readily adapted to other intra- or inter-species comparisons. DNA replication is an imperfect process which causes the mutations that give rise to genetic diversity during the evolution of genomes. While many mutations are independent, single-nucleotide substitutions or small insertions and deletions, some mutations arise as nonindependent clusters of substitutions and larger scale chromosomal rearrangements. Large-scale rearrangements  in particular can have a profound impact on genome evolution and contribute to both germline and somatic disease in humans. The replication-based mechanisms underlying structural variation typically involve a polymerase switch event in which a large segment of DNA is copied using a template from an alternate location in the genome. Methods for identifying these template switch mutations lack the power to detect smaller scale rearrangements which can arise through the same replication-based pathways. Here we outline a model which can detect and assess the statistical significance of such small-scale template switches within their evolutionary context. We show that these events are widespread in the evolution of great apes and that the genomic features associated with these small-scale rearrangements are similar to those of large-scale structural variants. Mutation clusters consisting of multiple nearby substitutions and indels (insertions and deletions) in sequence alignments are pervasive throughout eukaryotic genomes . These cIn humans, small-scale clustered mutagenesis is typically attributed to local mechanisms that occur during DNA replication, such as error-prone translesion synthesis and replication slippage , both ofThe template switch process inherent to all of these replication-based rearrangements involves the dissociation of the 3′ end of the nascent DNA strand and invasion of a physically-close alternate template. A period of replication using this alternate template is then followed by a second switch event in which the 3′ end of the nascent strand reassociates with the original strand , a seriein vivo during both eukaroytic and prokaryotic replication , called a match when xi = yj or a mismatch when xi ≠ yj), or a gap in one sequence . Probabilities are typically transformed into log-space for convenience, and the total probability of the alignment is the sum of the log-probabilities of each alignment column, yielding a global probability value for the most probable path through the pairHMM.PairHMMs are probabilistic models that emit a pair of aligned sequences given two input sequences j, …, ym . For eacIn a typical nucleotide (nt) sequence alignment, sequence homology under a pairHMM alignment is the alternate hypothesis, and the null hypothesis of no sequence homology may be rejected by comparing the global pairHMM alignment probability to that of a null alignment model in which the two sequences are emitted independently of each other . The occM2, and requires three combined recursions to fully decode the state path  as a test statistic for computing a measure of significance for the alternative hypothesis that a single template switch event occurred between input sequences We sought to establish a threshold on the LPR between the two generated alignments that maximises the recall of true template switch events and minimises the number of false positives caused by erroneously explaining a true cluster of substitutions and indels as an apparent template switch.To this end, we realistically simulated sequence evolution for human, chimpanzee and gorilla, and applied our alignment models to these simulated data. We simulated two types of evolution: first, without template switches (but with substitutions and indels) so as to determine the false positive level of our methods depending on the chosen LPR threshold. Second, we simulated sequence evolution in the presence of template switches, to investigate the detection power we can hope to achieve and assess the false negative rates for given thresholds (see Methods: Sequence simulations).t  , and “evn = 1503; For the purposes of subsequent analysis, we define two event sets of interest. First, the “unique” event set contains all 4017 of the significant events outlined above, allowing us to compare events discovered using our approach to that of . Second,We assessed how our method compares to that of Löytynoja and Goldman  in termsShort templated insertions are the most difficult class of rearrangement to capture in an evolutionary context, as many will plausibly present as a mutation cluster or short indel event in a multiple sequence alignment. Focusing on the gold-standard event set, our model largely captures and confidently explains such short templated insertions in the hominids whilst maintaining the ability to capture longer templated insertions . Few golFor each event, the ordering of the four switch points facilitates the description of post-event rearrangement patterns and the inference of intra-strand and/or inter-strand switching. Following Löytynoja and Goldman , we use As well as being unable to detect these ‘④ before ①’ events, Löytynoja and Goldman  assumed Using a fully probabilistic approach for template switch event discovery has enabled the identification of ~30% more significant and evolutionarily consistent events than an approach based on a constant scoring scheme coupled with conservative filtering, and has allowed us to assign statistical significance values to events in the final event sets. In addition, defining a gold-standard subset with fully resolved evolutionary directions has allowed us, for each event, to correctly define the ordering of switch points with respect to the ancestral sequence and infer the rearrangement pattern present in the descendant sequence. Using this larger set of significant events with resolved directions, we can better assess associations between event loci and a variety of genomic features in both the ancestral and descendant species.p < 0.01) of events within introns, transcription factor (TF) binding sites, and super enhancers . This may be a residual effect of the greater AT content in these regions compared to the random genomic sample , with a spike immediately around switch point ① (p < 2 × 10−16), with an increase at switch point ① that does not reach parity with genome-wide mean values , T10 , and YT2YT21 . In no tests did a motif with greater sequence complexity appear as more significantly enriched than AT rich sequence alone, suggesting that poly(dA:dT) tract DNA plays a more important role in event initiation than any more complex template switch associated motif. It is well established that such poly(dA:dT) tract DNA consisting of ⩾4–6 consecutive A:T base pairs causes intrinsic bending of the DNA molecule  being emitted from state hi . We use the symbol s to reflect that the logarithms of such values are often considered as emissions’ (additive) scores. For consistency, we represent all our parameters and calculations in terms of probabilities, with logarithms only introduced later (e.g. to create LPRs). In H are shown as nodes, non-zero elements of Q are shown as directed edges , and non-zero probabilities s are shown as annotated dashed arrows.We implement two models, a standard  pairHMM  and the M), insertion (I) and deletion (D) and, giving H = {M, I, D}. M corresponds to the emission of a pair of nucleotides ; no gaps can be emitted. I emits a gap and a nucleotide , and D emits a nucleotide and a gap . State transition probabilities Q are specified using two parameters, δ and ϵ, where δ is the frequency of indel events expected along a pairwise alignment and ϵ controls their lengths. We use δ = 1 − et(ρ/2)− and ϵ = 1 − 1/λ, where t is pairwise divergence measured in expected substitutions per site, λ is mean indel length and ρ is mean number of indel events per substitution. For the purposes of our hominid analyses, we set t based on estimates from . We then define a template switch event using the positioning of points ②, ③, and ④ relative to ① from a single high-confidence event randomly drawn from an event set generated between human (GRCh38) and chimpanzee (Pan_tro_3.0), using the model and filtering criteria of Please let us know if you have any questions while making these revisions.Yours sincerely,Jianzhi ZhangAssociate EditorPLOS GeneticsBret PayseurSection Editor: EvolutionPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: This manuscript is a pleasure to read. It is clearly written and reports a valuable development - using a probabilistic model, the authors substantially improve detection of template-switch events which masquerade as clusters of small-scale mutations. Should be published.My only suggestion is that the authors might want to search for footprints of template-switches in within-human polymorphisms. I understand very well that even ~1% differenes between humans and other great apes make detection of such switches in the course of their diveregence problemattc - we are encountering the same problem looking for microinversions on the human-chimpanzee path. Here, data on human polymorphisms can be really helpful - because with the decline of distance between the compared sequences the "noise problem" must diminish faster than linearly, because it takes multiple independent small-scale events to imitate a template switch. Difference between two human genotypes is ~0.1%, so, extrapolating from what the authors report, I must be heterozygous by dozens of template-switch mutations, which should be much easier to detect. Also, it might be interesting to screen complex de novo pathogenic events :1435-48) for template switches.Alexey Kondrashov p. 3 typo - "(25). the occurrence of a single template switch event is".p. 18 line 6th from bottom - "the find".Reviewer #2: Review of “Short-range template switching in great ape genomes explored using a pair hidden Markov model”In this manuscript, the authors develop a probabilistic model that can be used to identify clustered mutations caused by short-range template switching. This model builds upon previous work by the senior author, allowing for more accurate inferences in real datasets.Overall, I really enjoyed this study. The work is generally well explained and well done, and the results are not oversold or overinterpreted. Despite this positive view, I had a few concerns that I would like to see addressed:-The model selection procedure used here compares a "unidirectional" pairHMM with the template switch alignment ("TSA") pairHMM. The unidirectional pairHMM represents a kind of null model that only includes independent substitutions and indels, such that model selection is a comparison of the relative probabilities of the data under the two models. However, as mentioned in the Introduction, many clustered mutations are likely caused by error-prone polymerases, which cause both substitutions and indels.So my concern here is that both the simulations used to choose thresholds are inaccurate, and that inferences from data are also inaccurate. Is it possible that the TSA pairHMM is preferred when in fact the mutations are caused by error-prone polymerases?While I understand that the TSA pairHMM has to find a likely template nearby, there are no details given about how close this template must be . And since most clustered mutations are close together, it seems possible to find highly similar templates given enough search space. I also thought that the conclusions could have brought clustered mutations caused by error-prone polymerases back into focus, rather than implying that all clustered mutations were caused by template switching. Even if this is simply to contrast the smaller distances over which error-prone polymerases are likely to act relative to template switching, giving the reader a broader view would have helped.I would like to see these issues addressed, as well as additional details about what "short range" really means for this method.-This is a more minor issues, but: should we worry about the mosaic nature of genome assemblies? In other words, Clint (the chimpanzee used for the reference genome) is a diploid, but the assembly is represented as haploid. Should this fact make us more wary of inferences about intra- and inter-strand templating? I'm not clear on all the mechanisms, so wasn't sure if this is an issue.-The manuscript seems to report that ~94% of templated mutation clusters introduce either an insertion or deletion. Given this, I wondered how often this type of mutation would really be mistaken for sites of accelerated evolution--mostly alignments with indels are discarded from such scans. I would ask that the authors address this in the Conclusions.-Given the subject matter of the manuscript, I found it surprising that references to Schrider et al. , McDonald et al. , Harris and Nielsen , and Besenbacher et al.  were not included.Reviewer #3: This highly interesting paper adds rigor to the approach of Löytynoja & Goldman (2017) in identifying local mutation clusters that are plausibly explained by template-switching events. A template-switching event is a local indel (or cluster of substitutions) that is actually a nonlocal* inverted duplication, caused by an Okazaki fragment getting grabbed from some other part of the genome instead of the upcoming stretch of the lagging strand during DNA replication. I put an asterisk beside "nonlocal" because, although the duplicated sequence is typically nonlocal in the linear sequence, there is some justification for expecting it to be proximal in 3D space, given knowledge of the 3D structure of the chromosomes. There is in fact extensive study of the various structural mechanisms that can be involved in such events and the complex mutation patterns that can arise, which the present paper briefly reviews in the introduction, but the work itself uses a simpler model (following Löytynoja & Goldman 2017) which just allows the alternate template sequence to be copied from some finite-width neighborhood around the location of the event. This seems to be a reasonably compromise to using actual 3D proximity, and captures a lot of events.The basic outline of the paper is to present the model, assess its statistical power by simulation, apply it to identifying template-switching events in great ape genomic alignments, and discuss the phylogenetic placement of these events (and the related issue of reversibility). Compared to Löytynoja & Goldman (2017), the new probabilistic method is shown to detect more events and also to have better phylogenetic resolution of when the events occurred. The authors then investigate the properties of the DNA surrounding the "gold standard" subset of imputed template switch events that were unambiguous. This allows the authors to make data-justified claims about the structural properties of DNA around such events. The authors also analyze the proximity of their imputed template-switching events to regions of interest in human genome evolution, such as the "human accelerated regions" and "primate accelerated regions" identified in other studies, and report statistical associations with certain categories of annotated sequence features; perhaps unsurprisingly, this analysis seems to indicate a selection against such events within exons.All in all, this is very interesting work that makes a significant contribution to our collective understanding of the evolution of genomes, and the human genome in particular. The paper is extremely clearly written, and the scientific rationale is well-referenced and precise. I noticed only one typo .The critiques/comments/suggestions I have are mild, and mostly relate to the methodological exposition. Perhaps the most significant of these is that I do not believe the authors' algorithm for scoring template switch events (Supplementary Algorithm 2) is a Viterbi algorithm for a Pair HMM, as they claim. Rather it is a compilation of three recursions, each of which could be represented as a Pair HMM. The fact that the model switches to reverse-complement mode halfway through means that it can't be an HMM. However, taken together, I am pretty sure that these three recursions *can* be represented as a Pair Stochastic Context-Free Grammar (Pair SCFG) whose alignment path has been constrained to fit the supplied alignment. Such alignment-constrained Pair SCFGs have been used for other purposes; for example, noncoding RNA gene detection . A Pair SCFG can readily model a region of a pairwise alignment where the ancestral sequence has been inverted  so this would be the natural way to cast the model, I think.Why does this matter? Well, one assumes that the point of using standard terminology is to foster continuity with literature & thus maximize the accessibility and impact of the work. Given that Pair HMMs are very standard models, a natural way for a methods-inclined bioinformatics reader to approach this work would be to latch onto the description of the model and attempt to understand it through that lens. In fact, this is how I approached the paper, and found myself wondering "but how does the algorithm decide where the alternate template comes from? A Pair HMM can't do that". It turns out that the answer is that the algorithm actually involves three different recursions that are sort of being painted onto a Pair HMM, and this info is buried in the Supplement. By representing this as a Pair SCFG, the probabilistic formulation would be more rigorous. If the authors find that the Pair SCFG representation is not exactly identical to what they've done, but is close, then I think it'd be acceptable to say that their algorithm is an approximation to a Pair SCFG or is "Pair HMM-like". However misuse of statistically precise terms is somewhat endemic in our field and so I do think that the authors need to qualify that what they're calling a Pair HMM is not the standard Pair HMM that readers may be familiar with .Along these lines, thinking about probabilistic modeling rigour, it seems odd to me that - even though the point of this method is to develop a statistical test based on a Bayesian model comparison - the authors' algorithm does not sum over all possible locations of the template switch event, but rather uses a Viterbi algorithm to pick the single most likely location. Using the Forward algorithm instead  would give a clearer picture and might even boost the statistical signal for "there is a template switch event here" even in cases where there is insufficient statistical power to resolve the precise location of the alternate template . This is a nuance, and certainly doesn't invalidate what the authors of done; indeed a lot of their subsequent analysis involves is restricted to "gold standard" imputations where the location of the template switch is unambiguous, for which Viterbi-style maximum-likelihood inference is clearly sufficient. So, a minor detail perhaps, but I think worth noting.With regards to the simulation-based study of how well the method performs, the text mentions that "many template switch events are obfuscated by surrounding neutral mutations, allowing us to capture an average of 78%" of events. This is reminiscent of the "gap wander" phenomenon in pairwise alignment, whereby substitution events that occur near gaps can lead alignment algorithms to misplace the gaps; and if insertions and deletions occur near enough to one another, they can "cancel each other out" and the gap can be missed entirely. This leads to a statistical upper bound on the accuracy of sequence alignment. These phenomena can be analyzed quite rigorously, and in fact Gerton Lunter has derived a closed-form expression for gap wander under the Jukes-Cantor substitution model  including an analysis of the expected accuracy of human-mouse alignments. This makes me wonder whether these results could be useful in deriving some analytic bounds on the present method. For example, if the 78% figure that the authors report from their simulations could be related to a closed-form expression, it would be a slightly more useful result. I ought to emphasize that this is an open-ended mathematical challenge with no guarantees of a useful result, so I would not insist on it as a revision.To summarize: I find this a fascinating paper in evolutionary genomics, and a useful methodological advance that significantly improves on the previous work in this area by Löytynoja & Goldman. The results on DNA structure surrounding duplications, and associations with human sequence annotations, are interesting biologically. The work is generally presented very clearly, though I recommend that the authors distinguish Figure 2b from a true Pair HMM and perhaps (assuming they agree with my assessment of this) they might note that it is technically more similar to an alignment-constrained Pair SCFG. I would also recommend that they make some remark explaining why they chose Viterbi/CYK instead of Forward/Inside, and whether that is expected to affect the results. Finally, it might be interesting to try and understand their simulation results using Lunter's analytic theory of gap wander, though this may be a stretch and I wouldn't insist on it.Ian Holmes**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). 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Protein tertiary structure is important information in various areas of biological research, however, the experimental cost associated with structure determination is high, and computational prediction methods have been developed to facilitate a more economical approach. Currently, template-based modeling methods are considered to be the most practical because the resulting predicted structures are often accurate, provided an appropriate template protein is available. During the first stage of template-based modeling, sensitive homology detection is essential for accurate structure prediction. However, sufficient structural models cannot always be obtained due to a lack of quality in the sequence alignment generated by a homology detection program. Therefore, an automated method that detects remote homologs accurately and generates appropriate alignments for accurate structure prediction is needed. In this paper, we propose an algorithm for suitable alignment generation using an intermediate sequence search for use with template-based modeling. We used intermediate sequence search for remote homology detection and intermediate sequences for alignment generation of remote homologs. We then evaluated the proposed method by comparing the sensitivity and selectivity of homology detection. Furthermore, based on the accuracy of the predicted structure model, we verify the accuracy of the alignments generated by our method. We demonstrate that our method generates more appropriate alignments for template-based modeling, especially for remote homologs. All source codes are available at https://github.com/shuichiro-makigaki/agora. A protein’s structure often has a strong correlation with its function. Thus, the structure of a protein is an important piece of information in biological research. Protein structures can be determined experimentally using X-ray crystallography or nuclear magnetic resonance spectroscopy, and protein structures determined in this way are registered to, and accessible in the online Protein Databank (PDB) de novo prediction Homology modeling, or template-based modeling, is one of the computational protein structure prediction methods. It predicts protein structures based on the structure of a template protein and a sequence alignment between the target protein and the template protein. Template structures are the structures of homologous proteins (homologs), and are often found by a homology search of protein structure databases, such as the PDB. Currently, provided one can obtain a good template structure and generate an accurate sequence alignment, template-based modeling is the most practical structure prediction method. This is because the predicted models are more accurate than the other methods, such as To overcome this problem, the intermediate sequence search (ISS) method has been proposed to provide more distantly remote homology detection ISS is a useful technique for improving homology search sensitivity, and several studies have used this method for protein function prediction Sequence alignment generation of remote homologs is a difficult task due to low sequence identity between the query and target sequences. However, for the case of ISS results, we can use additional intermediate sequence information that bridges the two sequences requiring alignment. Thus, we hypothesize that the intermediate sequences would help to generate more accurate sequence alignments.In this paper, we propose a new sequence alignment generation method for remote homologs detected by an intermediate sequence search for sue in homology modeling. To our knowledge, this is the first study demonstrating the generation and evaluation of alignments using ISS results in the context of template-based modeling. To evaluate the quality of the generated sequence alignments, we performed homology modeling based on the sequence alignments and measured their structure prediction accuracy. As a result, the proposed method showed better accuracy in comparison to a baseline method. Our method is expected to be valuable for distant homologs, and we also evaluate our method for these distant and difficult pairwise targets.2To detect homologs, we use the intermediate sequence search method. To date, many ISS methods have been proposed to address early problems with the method 2.1Although the detection performance of the ISS is high, this method often provides many false positives However, it is difficult to generate alignments between remote homologs because the sequence identity between them is often low. Even if some local alignments can be generated, the length of the alignment region is often too short for accurate template-based modeling, such that prediction of the protein structure will not be accurate.To overcome the problem, we use intermediate sequences detected by the ISS. This is reasonable because, in the ISS phase, the proposed method only uses aligned sequence regions, while other domains in a sequence are not used for the search. At the final phase of alignment generation, the proposed method merges pairwise alignments between intermediate sequences. During the merging procedure, no dynamic-programming-based multiple sequence alignment method is used. Each sub-pairwise alignment is preserved, which means that the positions of residues in a pairwise alignment are preserved. A pairwise alignment between the query protein and one of the final hits is then split out from the merged alignment.This alignment method generally produces reasonable alignments, but sometimes generate obviously incorrect alignments because of a large shift of the aligned regions in intermediate search results and so on. Fortunately, we can detect most of these cases by checking the length of the aligned region. Therefore, in addition to merging multiple pairwise alignments, as described above, we also apply the Smith-Waterman algorithm to generate a pairwise alignment of the query and template. Then, we select one that has a longer aligned region.2.250, which was a reduced by clustering sequences of 50% sequence identity; and SCOP95 was used as the final database, reduced by 95% sequence identity clustering. DELTA-BLAST was used for the intermediate search tool. For merging intermediate alignments, we used MAFFT’s 40, which is a SCOP database reduced by 40% sequence identity. Each of the 100 sequences were randomly selected, one from each of the top 100 superfamilies that were sorted according to the size of the superfamily. The selected 100 test domains and their information are listed in Supplementary data.In this paper, we used the following datasets for evaluation. UniRef 2.3To evaluate alignment quality for template-based modeling, we generated structural models by template-based modeling from alignments obtained using the proposed method. For template-based modeling, we use the program MODELLER 3t-test. The p-value was As shown in In contrast, 44.1n and AUCn according to previous methods nth false positive, and AUCn was normalized by the number of false positives and cutoff value n. We define a true positive homology detection as results that are in the same superfamily as the query protein. By comparing the AUC of homology detection, we evaluated our method against DELTA-BLAST without ISS, as the baseline.In this study, we implemented a simple intermediate sequence search to avoid any influence from specific algorithms. Thus, the homology detection accuracy of the search method was unclear. To verify the accuracy of our implementation, we performed an evaluation test using the SCOP database. For the evaluation of detection accuracy, we used a receiver operating characteristic (ROC) curve, because of the imbalance between the number of homologs and non-homologs. For quantitative analysis, we employed the area under the ROC curve (AUC) as the evaluation metrics n), the AUC of both methods increased.4.25In this study, we developed an alignment generation algorithm suited for accurate template-based modeling based on intermediate sequence search (ISS) for remote homology detection. Our method used the intermediate sequence search method to detect remote homologs and a sum of similarity score to assign rankings. In the alignment generation phase, we proposed a method that extended the hit region detected by the ISS, and used multiple pairwise alignments between intermediate sequences. In addition to alignment generation, we also applied the pairwise Smith-Waterman algorithm to query and the template sequences, and selected one alignment based on the length of the aligned region. We evaluated our method by comparing the AUC of homology detection for sensitivity and selectivity. We also evaluated the quality of the alignments by comparing the accuracy of template-based structural models generated from the alignments. As a result, the proposed method could detect homologs more accurately than DELTA-BLAST without intermediates. The evaluation of alignment quality based on the accuracy of structural models generated from the alignment, revealed that the proposed method generates more appropriate alignments for template-based modeling, than those prepared without intermediate sequences. As for domains that are not detected by DELTA-BLAST, which were treated as difficult targets, model accuracy measured by TM-score improves by +0.04 on average, when compared using naïve dynamic programming-based alignment.In this study, we used a simple ISS model to generate alignments using intermediate sequences and evaluate the alignment quality. However, more intelligent ISS methods are available and if used in place of the simple ISS method used here, homology detection performance is expected to improve. Nevertheless, our alignment generation method can be successfully applied to the ISS. Evaluation of the more sophisticated ISS methods remains as one of future work.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Takashi Ishida: Conceptualization, Resources, Writing - review & editing, Supervision, Project administration, Funding acquisition.


Read alignment is central to many aspects of modern genomics. Most aligners use heuristics to accelerate processing, but these heuristics can fail to find the optimal alignments of reads. Alignment accuracy is typically measured through simulated reads; however, the simulated location may not be the (only) location with the optimal alignment score.Vargas implements a heuristic-free algorithm guaranteed to find the highest-scoring alignment for real sequencing reads to a linear or graph genome. With semiglobal and local alignment modes and affine gap and quality-scaled mismatch penalties, it can implement the scoring functions of commonly used aligners to calculate optimal alignments. While this is computationally intensive, Vargas uses multi-core parallelization and vectorized (SIMD) instructions to make it practical to optimally align large numbers of reads, achieving a maximum speed of 456 billion cell updates per second. We demonstrate how these ‘gold standard’ Vargas alignments can be used to improve heuristic alignment accuracy by optimizing command-line parameters in Bowtie 2, BWA-maximal exact match and vg to align more reads correctly.https://github.com/langmead-lab/vargas under the MIT license.Source code implemented in C++ and compiled binary releases are available at Bioinformatics online. Biological gold standards, such as the Platinum Genomes  (‘vector’) operations can be employed that operate on a group of values at once instead of a typical processor instruction that operates on individual scalar values. Vargas implements a form of the Smith-Waterman  algorithVargas is the most efficient and flexible tool for establishing computational gold standards for evaluating read alignment heuristics and scoring schemes. While the need for such standards was discussed and partially addressed in the previous Rabema study ] and Xeon Platinum [Skylake (SKX)]. The KNL architecture supports 256–288 threads across 64–72 cores  and SKX https://github.com/cdarby/vargas-experiments.First, we used Vargas to study the performance and accuracy of heuristic read aligners on real sequencing data. While most benchmarks use simulated reads, using a heuristic-free aligner allows us to determine whether the alignment for a real read is correct-by-score, as others have observed . FinallyWe measured performance using GCUPS (giga cell updates per second), which is the number of cells in the dynamic programming matrix computed per second. This standard metric used extensively in the literature normalizes performance to enable comparisons between experiments conducted using reads and references of different lengths . KNL empThe best speed we observed using Vargas is 456 GCUPS, which was observed for SG alignment to the linear reference of chromosome 19 using AVX512BW instructions with 64-way vectorization on 48 threads of the SKX computer. When the chromosome 19 graph contained all 1000 Genomes Phase 3 SNPs and indels (5.1 million nodes and 3.66% increase in linearized genome size) we observed 237 GCUPS with the same configuration. When aligning 150 bp reads to chromosome 10, SeqAn  recordedVargas SG alignment to the linear genome on Xeon Phi (KNL) with AVX2 instructions and 271 threads achieved a maximum speed of 194 GCUPS. Alignment to the all-variant graph described above achieved 110 GCUPS with the same configuration. This compares favorably to previous Xeon Phi-based efforts such as SWAPHI (58.8 GCUPS) , as wellWe use 100 000 unpaired 100 bp reads from the 1000 Genomes Project sample NA18505, SRA accession ERR239486 and 100 000 unpaired 250 bp reads from the 1000 Genomes Project sample NA19017, SRA accession SRR1295544. All aligners were evaluated with respect to the human reference genome GRCh38 primary assembly. Since HISAT2 and vg have graph alignment capabilities, we also built graph-genome indexes for both using the 6.2 million SNPs and indels from the 1000 Genomes Phase 3 call set with allele frequency at least 10% , -R (re-seeding), -L (seed length) and -i (seed spacing) parameters. We found that these presets effectively trade between time and accuracy when evaluated using correct-by-score. For the settings that are faster but less accurate, the number of reads failing to align increases and the number of reads aligned correctly decreases. Correctness decreases approximately linearly with optimal alignment score (nt score . InteresHISAT2. The latest version of HISAT2 (version 2.2.0-beta https://github.com/DaehwanKimLab/hisat2/tree/hisat2_v2.2.0_beta) provides two modes that change the –score-min  and –bowtie2-dp parameters to align more reads. Very-sensitive uses ‘unconditional dynamic programming’ and sensitive uses ‘conditional dynamic programming’. Compared to the default ‘fast’ mode, more reads are correct-by-score using the very-sensitive and sensitive modes, but more reads are incorrect-by-score. This is possible because more reads are aligned. In contrast, the slowest Bowtie 2 and BWA-MEM presets have the least incorrect reads  and -r (re-seeding) that trade between speed and accuracy. With the default parameter settings (blue line) and our proposed presets, correctness decreases with optimal score for reads with score greater than 50, but then increases with optimal score for reads with optimal score between 50 and 30, the minimum alignment score reported . In This case study demonstrates how a set of real reads annotated with the optimal alignment score can be used to tune heuristic alignment parameters that are exposed to the user on the command line. While a 1–2% increase in correctly scored alignments may seem marginal, this would have a significant impact on a dataset with millions or billions of reads and on use cases with a low signal-to-noise ratio such as cell-free DNA analysis or somatic variant calling. The parameters enabling this increase in accuracy can be identified with Vargas alignments of just a few thousand reads.We also performed a similar optimization experiment for Bowtie 2, BWA-MEM and vg with graph genome using ChIP-seq reads, described in the computational gold standard for evaluating short-read alignment algorithms, including with real sequencing datasets and in much the same way as biological gold standards are used to assess variant calling algorithms. Vargas opens the door to comprehensive study of the effects of alignment heuristics and, distinct from that, the effects of alignment scoring functions. Though the default scoring functions of tools like BWA-MEM and Bowtie 2 are widely used, they are not very well studied, and this is in large part because it is difficult to separate the effect of the scoring function from the closely related effects of the heuristics. Vargas alignments could also be used to evaluate the effects of different reference genomes on alignment accuracy, such as comparing graph genomes containing different variant sets to each other and to linear references, as investigated using simulation in the FORGe study  and parameters , and with both linear and graph references. Read alignments produced by Vargas can be used as a k disregards reads where the optimal match is further than k from the reference, which we show are the most error-prone in SG alignment algorithms. Future benchmarking efforts comparing alignment algorithms, parameter settings and graph versus linear reference genome paradigms can now be based on aligner-specific, real data computational gold standards generated using Vargas.The Rabema study of Alignment methods developers can use Vargas alignments to identify particular reads where the heuristic fails to find the optimal solution to the optimization problem posed, and can revise the heuristic strategies accordingly. Knowing optimal alignments for a subset of the input reads can serve as training data for identifying optimal alignment parameters, as tools like Teaser (Many short-read datasets use paired-end reads, where a DNA fragment is sequenced from both ends, typically with a few hundred bases between the read pairs. Heuristic aligners account for the fact that pairs should align concordantly to the reference, i.e. in a particular expected configuration based on the library preparation. Since concordance is not defined by the scoring function per se, and since checking for concordance of paired-end alignments can be implemented as a post-pass after each end has been aligned individually, we left paired-end alignment to future work. We evaluated Salmon’s RNA-seq alignments to the transcriptome, but Vargas’ optimization functions do not extend to spliced short-read alignment, such as aligning a RNA-seq read to a genome. Possible optimization functions for penalizing splicing events could depend on intron length, genomic nucleotides at the donor and acceptor sites and a transcriptome annotation. Extending the dynamic programming model to find optimal solutions would require that every position in the read could be spliced to any pair of coordinates in the genome with a corresponding alignment penalty, exponentially increasing the possibilities to be explored. Vargas does not currently support the minimum-of-two-affine-functions gap scoring function used by Minimap2  or variaBecause Vargas calculates all possible alignments of read to reference in the course of filling the dynamic programming matrix, every match above a certain minimum score could be reported. This could be useful in CRISPR guide RNA off-target analysis: current approaches are limited to a few mismatches; using Vargas would allow for a full edit distance scoring function and enumeration of distant alignments, including in the presence of genetic variation. Fully profiling all possible alignments of a read, or a substring extracted from the genome, could also be applied to the problem of characterizing mappability as explored in btaa265_Supplementary_DataClick here for additional data file.


Conotoxins are short, cysteine-rich peptides of great interest as novel therapeutic leads and of great concern as lethal biological agents due to their high affinity and specificity for various receptors involved in neuromuscular transmission. Currently, of the approximately 6000 known conotoxin sequences, only about 3% have associated structural characterization, which leads to a bottleneck in rapid high-throughput screening (HTS) for identification of potential leads or threats. In this work, we combine a graph-based approach with homology modeling to expand the library of conotoxin structures and to identify those conotoxin sequences that are of the greatest value for experimental structural characterization. The latter would allow for the rapid expansion of the known structural space for generating high quality template-based models. Our approach generalizes to other evolutionarily-related, short, cysteine-rich venoms of interest. Overall, we present and validate an approach for venom structure modeling and experimental guidance and employ it to produce a 290%-larger library of approximate conotoxin structures for HTS. We also provide a set of ranked conotoxin sequences for experimental structure determination to further expand this library. Conus magus [Conus bullatus, as a competitive agonist for the lysophosphatidic acid receptor 6, a G-protein coupled receptor involved in the development of several cancers [Toxins have for a long time been considered a rich natural source of therapeutic leads because of their high specificity and binding affinity for various receptors involved in different biological pathways ,2. The dus magus . The on-us magus —means thus magus ,6,7. Shous magus . In one  cancers . Aside f cancers . Thus, i cancers . Thus, s cancers .If the structures of proteins could be rapidly predicted strictly from their sequences, structural determination would not be a bottleneck; however, structure prediction from sequence still remains a challenging proposition . Ab initIn this article, we employ a simple sequence graph-based algorithm for homology modeling of related toxin sequences and use the resultant graph to suggest a set of sequences of interest for experimental structural characterization. Network or graph theory is, broadly speaking, the study of the ways in which different types of objects may be related to one another . In esseSince sequences of proteins may be related in many different ways, including simple amino acid identity and evolutionary relationships, it is no surprise that graph theory has a long and storied history of usage for sequence-grouping tasks such as homology detection , structuIn the following sections, we present our graph-based approach and employ it to construct sequence graphs and identify good libraries of templates for homology modeling. We demonstrate that, despite the known relationship between the conotoxins, these libraries improve outcomes for structure homology modeling over using a 25% flat cutoff (plus 5% padding). We use our sequence graphs to construct a set of tables indicating sequences in which experimental structural characterization is predicted to be most valuable in creating a broad structure library by using homology modeling. Finally, we employ the graphs and libraries as part of a homology modeling procedure that results in a library of low-resolution structures for the conotoxins that will be of use in future high-throughput studies.We initialize the algorithm see  in whichcomplete . We haltIn These figures are a graphical illustration of the sequence space of conotoxins that may be used as a guide to experiment or to produce low-resolution structures for initial HTS. Specifically, out of the 801 sequences with four cysteines, 61  currently have experimentally-resolved structures. The graph-based approach selected 49  of these structures as comprising the four cysteine template library . Maintenance of the tertiary fold of conopeptides, particularly after their secretion into the extracellular environment, depends more on its disulfide bonds and less on the presence of a hydrophobic core . As suchOne important point about short, disulfide-rich peptides that we have not addressed in this work is the existence of so-called “disulfide isomers.” Under certain environmental conditions, there is experimental evidence suggesting that some toxins do not exist as a single set of “native” structures but as a heterogeneous—perhaps metastable—ensemble populated with strikingly different conformations corresponding to differing patterns of cysteine connectivity . AlthougAlthough we do not directly address the question of structure–function relationships in this work, the graph-based method does suggest an interesting route forward. It has been shown that in addition to strict structural contributions, conotoxin binding is also heavily related to surface electrostatics , and, inFor use in construction of the template libraries, we employed a set of 142 conotoxin structures downloaded from the PDB , which wFor use in the analysis detailed in this article, we downloaded a set of 6255 peptide sequences from the Conoserver  using thL is the length of the alignment in numbers of amino acid residues and we set For each subset of sequences corresponding to a different number of contained cysteines, we created an alignment graph as follows. For every sequence, we computed a pairwise alignment with every other sequence, using the “PairwiseAligner” class in the “Align” module of the Biopython package , in globWe constructed two different template libraries for each subset of sequences, one from the pairwise alignment graph and one from a static 25%-identity cutoff . The best homology model was chosen as the one with the lowest backbone RMSD to the known or experimentally-resolved structure, using the “align” command in PyMOL  that supAfter assessing the quality of the template libraries, we used them to construct via homology modeling a database of structures for those conotoxin sequences  score  and the D returned by the test is such that n and m are the number of samples in each set, respectively. The analysis is referred to in We use the Kolmogorov–Smirnov two-tailed test as implemented in the SciPy package  and refeOverall, the work in this article presents a rational graph-based algorithm that we employ to expand the repertoire of known conotoxin structures for application in a high-throughput manner as part of the early stages of drug design. We expect that the libraries, the expanded set of structures, and the ranking of sequences in terms of the degree of connectedness to other sequences will be valuable resources improving the prospects of conotoxins as novel therapeutic leads and that our approach may be employed for initial characterization of other sets of evolutionarily-related toxins.