Protein complexes are the main functional modules in the cell that coordinate and perform the vast majority of molecular functions. The main approaches to identify and quantify the interactome to date are based on mass spectrometry (MS). Here I summarize the benefits and limitations of different MS-based interactome screens, with a focus on untargeted interactome acquisition, such as co-fractionation MS. Specific emphasis is given to the discussion of discovery- versus hypothesis-driven data analysis concepts and their applicability to large, proteome-wide interactome screens. Hypothesis-driven analysis approaches, i.e., complex- or network-centric, are highlighted as promising strategies for comparative studies. While these approaches require prior information from public databases, also reviewed herein, the available wealth of interactomic data continuously increases, thereby providing more exhaustive information for future studies. Finally, guidance on the selection of interactome acquisition and analysis methods is provided to aid the reader in the design of protein-protein interaction studies. Proteins are the main functional molecules within the cell. However, most proteins do not act in solitude. Instead, they often interact with other proteins to form large macromolecular assemblies, which coordinate and perform the majority of molecular functions inside the cell. Many protein complexes are evolutionarily conserved  and theiImportantly, this review mainly focuses on the physical interactome. Thus, a PPI generally refers to a physical interaction between two proteins, unless otherwise stated. A PPI network consequently represents a set of proteins that are connected to each other via PPIs. A protein complex, in turn, refers to a stable group of physically interacting proteins. However, the physicochemical boundaries for differentiating a stable protein complex from more dynamic functional modules in the cell still remain to be defined. In context of this review, clusters of co-regulated PPIs are termed protein complexes.Over the last decades, mass spectrometry (MS)-based proteomics has emerged as the main approach for studying both the proteome ,4 and itMS-based workflows for acquiring interactome data can broadly be categorized into two groups: (1) targeted approaches, which focus on a single protein and its interactions at a time, and (2) untargeted interactome screens, which aim to investigate the entire compendium of protein complexes in a given sample. Below I summarize different strategies of each category, focusing on the benefits and limitations of each approach. The most common MS based technology to study PPIs is affinity purification coupled to MS (AP-MS) ,7. Here,To capture more transient interactions, proximity labelling approaches such as BioID  and APEXAs discussed above, AP-MS and proximity labelling are inherently targeted approaches, which can only create whole interactome information through combination of a large number of experiments. In contrast, there are a number of methods that are directly geared towards more holistic, untargeted measurements of the interactome, including cross-linking or co-fractionation coupled to MS, thermal proteome profiling and full proteome co-variation analysis. For these techniques, the coverage (number of protein complexes identified) depends solely on the optional application of prior purification steps and technical limitations of the MS instrument and acquisition itself.Drosophila melanogaster embryo [In cross-linking coupled to MS (XL-MS), a cross-linking reagent is added to the sample, which covalently links amino acid residues (commonly lysines) that are close to each other. Traditionally, XL-MS has mainly been applied to purified proteins and protein complexes, with the goal to gain structural information . Howeverr embryo . Currentr embryo . Additior embryo ,20.An alternative approach to gain holistic interactome information in an untargeted fashion is based on complex co-fractionation coupled to MS (CoFrac-MS). Originally developed and applied with the goal to map proteins to different cellular organelles ,22, CoFrSimilar to CoFrac-MS, thermal proteome profiling (TPP) also offers a global strategy to assess PPIs and protein complexes. TPP is a method to systematically measure protein thermal stability and abundance . Here, aA further, more indirect strategy for investigating protein complexes in an untargeted and holistic fashion is to perform full proteome co-variation analysis. Here, data is typically not specifically acquired for protein complex analysis. The basis for full proteome co-variation studies are large MS datasets, which can be computationally leveraged to investigate co-variation of proteins. This strategy has been applied to identify evolutionarily conserved or highly variable protein complexes across species and individuals ,35. The In summary, MS-based workflows for interactome profiling can be categorised into targeted (AP-MS and proximity labelling) and untargeted  interactome acquisition strategies. The targeted approaches require a large number of reciprocal experiments to obtain a holistic view of the interactome of a single sample. In contrast, XL-MS, CoFrac-MS and TPP, in principle, enable the inference of a PPI network from a single sample in one experiment . Full proteome co-variation analysis, by definition, cannot be performed on a single sample, but requires large MS cohorts of hundreds of individual samples. Here, multiplexed data acquisition strategies such as stable isotope labeling with amino acids in cell culture (SILAC) provide the only possibility to reduce the number of required MS runs. Differently to targeted interactome acquisition strategies, the proteome and interactome coverage of untargeted interactome screens is solely determined by current technical state of the art, but not by the acquisition concept itself.A major challenge in untargeted interactome screens, especially in CoFrac-MS, TPP and full proteome co-variation analysis, is to differentiate between signals originating from protein complexes and signals derived purely from random co-elution/co-variation. In this section I describe the two main approaches for analyzing data from untargeted interactome screens: (1) discovery- and (2) hypothesis-driven data analysis. The most widely applied data analysis strategy for untargeted interactome screens is the discovery approach. Similar to discovery proteomics, where the goal is the identification of a protein, discovery analyses in interactomics are focused on the identification of novel protein complexes in a given dataset. The analysis concept is based on calculating pairwise scores  between all detected proteins, followed by a classification task that determines which protein-pairs likely represent a true interaction. The classification is commonly achieved by machine learning (ML) algorithms that are trained based on a positive and negative reference set of interactions ,36,37. Pcomplex-centric data analysis, a protein complex database is used as prior information to directly test for evidence of specific protein complexes in the dataset at hand. Here, only a small fraction of the pairwise comparisons are necessary, thereby markedly reducing the search space and the risk of false positives. Complex-centric analysis of interactome data can be regarded as analogous to peptide-centric data analysis of proteomics data generated by data-independent acquisition (DIA) [Targeted  data analysis strategies provide an alternative approach to address the challenges associated with untargeted interactome screens. Prior knowledge about known PPIs or protein complexes is leveraged to increase the sensitivity, selectivity, and consistency of quantitative information. In on (DIA) ,45. In pon (DIA) . HoweverWe recently applied complex-centric analysis to map the interactome of the human HEK293 cell line in a single CoFrac-MS experiment ,42. We unetwork-centric rationale. Here, an entire PPI network is leveraged as prior information. Instead of looking at groups of proteins together , the network-centric approach evaluates individual edges in a network to test if the edge is supported by the given data, for example, by a positive correlation of intensities across fractions. The reference PPI network is thereby updated during the process, finally representing only condition-specific interactions. The main benefit of the network-centric analysis concept compared to discovery approaches is that its search space is significantly reduced, thus boosting sensitivity and selectivity. However, in contrast to complex-centric analysis, no protein complex inference  is conducted, which improves robustness against technical effects and allows prior knowledge to be less strictly defined, also including low confidence interactions in a PPI network. These properties make the network-centric approach particularly scalable across multiple conditions and larger datasets, where protein complex subunits may not be equally measurable across all conditions. We recently developed and applied a network-centric strategy for analyzing CoFrac-MS data across different cell cycle stages in the HeLa cell line [An alternative hypothesis-driven approach for untargeted interactome screens follows a ell line . Althougell line . This inIn summary, analysis strategies for untargeted interactome screens can be divided into discovery- and hypothesis-driven approaches. The main benefit of discovery approaches is that they enable the identification of novel complexes without requiring prior knowledge. However, they suffer from reduced sensitivity and selectivity in single experiments. Hypothesis-driven strategies overcome this challenge by using prior knowledge to reduce the search space and allow consistent detection and quantification of interactions and protein complexes across conditions. The main limitation of hypothesis-driven strategies is the necessity to have access to prior knowledge of sufficient depth and quality. Complex-centric analysis might require the use of graph partitioning algorithms, such as ClusterOne , to deriAs discussed above, hypothesis-driven analysis approaches offer many benefits. However, their successful application depends on the availability of prior knowledge based on PPI or protein complex databases. Here, I review the most commonly applied databases, including the type of information they contain , the organisms they cover and their comprehensiveness. Please note that I cover only a selection of the most comprehensive, widely used and recently updated PPI databases (for an inclusive review see ). Table A. thaliana, S. cerevisiae, E. coli, C. elegans and SARS-CoV-2. HuMap is a third protein complex database, containing only human complexes. In contrast to CORUM and Complex Portal, HuMap is not manually curated, but contains protein complex information derived from the integrative analysis of over 15,000 mass spectrometry experiments covering AP-MS, proximity labelling and CoFrac-MS measurements [Protein complex databases generally include modules of physically interacting proteins that have been experimentally observed. The most commonly used, gold-standard database for protein complex information is the CORUM database, which contains 4274 manually-curated protein complexes across different mammals, mainly human (67%), mouse (15%) and rat (10%) . An alteurements . HuMap cAmong available PPI databases solely based on experimental data, IntAct  and BioGMany different interactome acquisition and analysis approaches, as well as available prior knowledge, have been discussed, but the question remains how to decide between these different strategies. To guide the reader in experimental design, I here present different scenarios based on alternative research questions and provide advice for the most appropriate interactome acquisition and analysis approaches.Targeted interactome acquisition approaches are usually the method of choice for studies aiming to investigate the interaction partners of a single protein, a specific pathway or a submodule in the cell. If stable, physical interactions are of interest, AP-MS should be used. If transient interactions or short, time-resolved processes are of interest, proximity labelling may be preferred. Given the constraints of keeping experimental costs and time in a feasible range, a trade-off must be made between the number of investigated bait proteins and the number of samples .XL-MS is the leading MS-based method for structural studies in which the interest is not only the protein complexes themselves, but also their three-dimensional structures and binding interfaces. However, system-wide XL-MS studies are still technically limited in their achievable proteome and interactome coverage . TherefoThe above-mentioned scenarios are geared towards studies that aim to identify or quantitatively compare interactomes across specific samples, with the capacity to acquire exclusive data for the analysis. For more general systems biology questions, where patterns rather than explicit examples are of interest , full proteome co-variation analysis can provide sufficient resolution to gain valuable insights. Importantly, systematic co-variation studies across full-proteome datasets can enable the inference of protein complex level information for cohorts with sample amounts that are too limited for specific interactome data acquisition, for example, in clinical studies. While discovery analysis in such datasets is hampered by an exploding search space, complex-centric analysis approaches have been shown to provide valuable results ,35.The identification of protein complexes and investigation of their dynamic rewiring across biological conditions is an active area of research that is of interest in both basic and translational science. To date, AP-MS remains the gold standard for generating high confidence interactome maps. However, the necessity to perform a separate experiment for every bait provides limited scalability for systems biology studies. Due to its multiplexed experimental design, CoFrac-MS has become a very promising strategy to perform comparative interactome screens. Technical improvements, especially to MS instrumentation, data acquisition and analysis, will continue to improve the proteome coverage achievable in single MS runs, exemplified by recent break-throughs with novel DIA technologies ,65. Rece


Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimised multiple sequence alignments, generate models with acceptable quality (DockQ ≥ 0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR. Predicting the structure of protein complexes is extremely difficult. Here, authors apply AlphaFold2 with optimized multiple sequence alignments to model complexes of interacting proteins, enabling prediction of both if and how proteins interact with state-of-art accuracy. Such interactions vary from being permanent to transient3. Some protein–protein interactions are specific for a pair of proteins, while some proteins are promiscuous and interact with many partners. This complexity of interactions is a challenge both for experimental and computational methods.Protein–protein interactions are central mediators in biological processes. Most interactions are governed by the three-dimensional arrangement and the dynamics of the interacting proteins5. Protein docking methodologies refer to how proteins interact and can be divided into two categories considering proteins as rigid bodies; those based on an exhaustive search of the docking space6 and those based on alignments (both sequence and structure) to structural templates7. Exhaustive approaches rely on generating all possible configurations between protein structures or models of the monomers9 and selecting the correct docking through a scoring function, while template-based docking only needs suitable templates to identify a few likely candidates. However, flexibility has often to be considered in protein docking to account for interaction-induced structural rearrangements11. Therefore, flexibility limits the accuracy achievable by rigid-body docking12, and flexible docking is traditionally too slow for large-scale applications. A possible compromise is represented by semi-flexible docking approaches13 that are more computationally feasible and can consider flexibility to some degree during docking.Often, studies of protein–protein interactions can be divided into two categories, the identification of what proteins interact and the identification of how they interact. Although these problems are distinguished, some methods have been applied to both problems14. Alternatively, a recent benchmark study8 reports SRs of different web-servers reaching up to 16% on the well-known Benchmark 5 dataset15.Regardless of different strategies, docking remains a challenging problem. In the CASP13-CAPRI experiments, human group predictors achieved up to 50% success rate (SR) for top-ranked docking solutions16. Thanks to an advanced deep learning model that efficiently utilises evolutionary and structural information, this method consistently outperformed all competitors, reaching an average GDT_TS score of 9016. Recently, RoseTTAFold was developed, trying to implement similar principles17. Since then, other end-to-end structure predictors have emerged using different principles such as fast multiple sequence alignment (MSA) processing in DMPFold218 and language model representations19.Recently, in the CASP14 experiment, AlphaFold2 (AF2) reached an unprecedented performance level in structure prediction of single-chain proteins21. Later, these methods were improved using machine learning22.As an alternative to other docking methods, it is possible to utilise co-evolution to predict the interaction between two protein chains. Initially, direct coupling analysis (DCA) was used to predict the interaction of bacterial two-component signalling proteins23). In this pipeline, the interaction between two chains from a heterodimeric protein complex and their structures were predicted using distance and angle constraints from trRosetta25. This study demonstrated that a pipeline focused on intra-chain structural feature extraction can be successfully extended to derive inter-chain features as well. Still, only 7% of the tested proteins were successfully folded and docked.In a Fold and Dock approach, two proteins are folded and docked simultaneously. We recently developed a Fold and Dock pipeline using another distance prediction method focused on protein folding , resulting in noise masking the sought after co-evolutionary signal, while too shallow alignments do not provide sufficient co-evolutionary signals.In that study, we found that generating the optimal MSA is crucial for obtaining accurate Fold and Dock solutions, but this is not always trivial due to the necessity to identify the exact set of interacting protein pairs27 and our protocol performs quite close to (63% vs 72%) the recently developed AF-multimer28, which was developed using the same data as the test set here, making a direct comparison difficult.In this work, we systematically apply the AF2 pipeline on two different datasets to Fold and Dock protein–protein pairs simultaneously. We explore the docking success using the AF2 pipeline in combination with different input MSAs, in order to study the relationship between the output model quality and these inputs. We also find that, by scoring multiple models of the same protein–protein interaction with a predicted DockQ score (pDockQ), we can distinguish with high confidence acceptable (DockQ ≥ 0.23) from incorrect models. The modelling success is higher for bacterial protein pairs, pairs with large interaction areas consisting of helices or sheets, and many homologous sequences. We also test the possibility to distinguish interacting from non-interacting proteins and find that, using pDockQ, we can separate truly interacting from non-interacting proteins with consistent accuracy. We find that the results in terms of successful docking using AF2 are superior to other docking methods. AF2 clearly outperforms a recent state-of-the-art methodThe SR, i.e., the percentage of acceptable models (DockQ > 0.23), is used to measure AF2 performance over the development set (216 proteins) using the different MSAs. The best performance is 33.3% for the AF2 MSAs and 39.4% for the AF2+ paired MSAs  with the fine-tuned versions of (model_1_ptm). Surprisingly, the original AF2 model_1 outperforms AF2 model_1_ptm in most cases (Table  with theThe best model and configuration for AF2 m1-10-1) was used for further studies on the test set. The best outcome using this modelling strategy results in an SR of 57.8% (856 out of 1481 correctly modelled complexes) for the AF2 + paired MSAs compared with 45.0% using the AF2 MSAs alone  and incorrect models provided by several metrics using a receiver operating characteristic (ROC) curve. Different criteria were examined over the test set, including (i) the number of unique interacting residues (Cβ atoms from different chains within 8 Å from each other) in the interface, (ii) the total number of interactions between Cβ atoms in the interface, (iii) the average plDDT for the interface, (iv) the lowest plDDT of each single-chain average, and (v) the average plDDT over the whole protein heterodimer Fig. . Three cInterestingly, the average plDDT of the entire complex only results in an AUC of 0.66, suggesting that both single chains in a complex are often predicted very well, while their relative orientation may still be incorrect.Figure Five models are generated using the best strategy (m1-10-1 with AF2 + paired MSAs) with different initialisation (random seeds). The average SR (57.2% ± 0.0%) is similar for all five runs. However, the average deviation for individual models is DockQ = 0.08 when comparing the best and worst models for a target Fig. , i.e., tIn the test set, about 60% of the complexes can be modelled correctly. We try to identify what distinguishes the successful and unsuccessful cases by analysing different subsets of the test set. First, we divide the proteins by taxa, next by interface characteristics and finally by examining the alignments.Saccharomyces cerevisiae is better than for Homo sapiens  in both paired and AF2 MSAs was analysed. It is clear that the fraction of correctly modelled sequences increases with larger Neff scores Fig. . Also, pChains derived from CASP14 heteromeric targets and chains from PDB complexes with no templates are folded in pairs using the presented AF2 pipeline .6TMM  has an interface of just 19 residue pairs.For the CASP14 chains, four out of six pairs display a DockQ score larger than 0.23 (SR of 67%). No ranking is necessary in this case, given that all produced docking models for the same chain pair are very similar (the average standard deviation is 0.01 between each set of DockQ scores). An interesting unsuccessful docking is obtained modelling chains from the complex with PDB ID 7EIV_A-C]) and B (7MEZ_A-B). More interesting, in one of the incorrect models , Fig. The SR for docking the proteins without templates is 50%. Between the five different initialisations, the average difference in the DockQ score is 0.03, and there is no deviation in SR, i.e., ranking did not improve the SR. Two acceptable models are displayed in Fig. A-E Fig. , the shoEscherichia coli and the 1481 truly interacting proteins from the test set with an AUC of 0.87. Another recently published method obtains AUC 0.76 on this set27. However, these results are probably overstated since the negative set only contains bacterial proteins, while the positive set is mainly eukaryotic.Using the best separator from the model ranking, the pDockQ, it is possible to distinguish the 3989 non-interacting proteins from 31 combined with the non-interacting proteins from E. coli. On this combined set of 1481 interacting and 5694 non-interacting proteins, we obtain an AUC of 0.82 for the average interface plDDT and slightly higher (0.84 and 0.85) for the number of interface contacts and residues, respectively  can predict the structure of many heterodimeric protein complexes, although it is trained to predict the structure of individual protein chains. Even using the default settings, it is clear that AF2 is superior to all other tested docking methods, including other Fold and Dock methods24, methods based on shape complementarity32 and template-based docking. Using optimised MSAs with AF2, we can accurately predict the structure of heterodimeric complexes for an unprecedented SR of 62.7% on a large test set. The SR is higher in E.coli (76.4%) than in H. sapiens or S. cerevisiae (58.1% and 66.2% respectively).Here we show that AlphaFold233 (pDockQ) with an average error of 0.1, resulting in the separation of acceptable and incorrect models with an AUC of 0.95. This means that 31% of the models can be called acceptable at a specificity of 99% (or 54% at 90% specificity). Interestingly, no additional constraints are implemented in AF2 to pull two chains in contact, meaning that chain interactions (and subsequently interface sizes) are exclusively determined by the amount of inter-chain signals extracted by the predictor. Assuming that all residues in an interface contribute to the interaction energy could explain why larger interfaces are more likely to be correctly predicted.Further, by analysing the predicted interfaces, we can predict the DockQ score34 runs on Uniclust3035 instead of using the MSAs from AF2. Fast MSA generation circumvents the main computational bottleneck in the pipeline. Using pDockQ makes it possible to separate truly interacting from non-interacting proteins with an AUC of 0.87, making it possible to identify 51% of interacting proteins at an error rate of 1%. The pDockQ score discriminates between both model quality and binary interactions. Therefore, the same pipeline can identify if two proteins interact and the accuracy of their structure.We find that the MSA generation process can be sped up substantially at no performance loss (performance increase of 1% SR) by simply fusing MSAs from two HHblits36. If 31% of these can be predicted at an error rate of 1%, this results in the structure of 19,842 human heterodimeric protein structures. The computational cost to run all of this is ~5 days on an Nvidia A100 system and has since the development of the pipeline presented here, deemed FoldDock, been applied37.Never before has the potential for expanding the known structural understanding of protein interactions been this large, at such a small cost. There are currently 64,006 pairwise human protein interactions in the human reference interactome38 is used to develop the pipeline, focusing on the AF2 configuration presented here. This set contains protein pairs, with each chain having at least 50 residues, sharing <30% sequence identity and no crystal packing artefacts. There are 219 protein interactions for which both unbound (single-chain) and bound (interacting chains) structures are available. Unbound chains share at least 97% sequence identity with the bound counterpart and, to facilitate comparisons, non-matching residues are deleted and renumbered to become identical to the unbound counterpart. AF2 MSAs could not be generated for three of the complexes due to memory limitations  using a computational node with 128 Gb RAM for the MSA generation and were thus disregarded, resulting in a total of 216 complexes. The dataset consists of 54% Eukaryotic proteins, 38% Bacterial and 8% from mixed kingdoms, e.g., one bacterial protein interacting with one eukaryotic.A set of heterodimeric complexes from Dockground benchmark 427 to test the developed pipeline. Here, three large biological assemblies were excluded. These complexes share <30% sequence identity, have a resolution between 1–5 Å and constitute unique pairs of PFAM domains (no single protein pair have PFAM domains matching that of any other pair). Some structures failed to be modelled for various reasons (see limitations of data generation), resulting in a total of 1481 structures. These proteins are mainly from H. sapiens (25%), S. cerevisiae (10%), E. coli (5%) and other Eukarya (30%).We used 1661 protein complexes with known interfaces from a recent study5AWF_D-5AWF_B, 2ZXE_B-2ZXE_A and 2ZXE_A-2ZXE_G) report “ValueError: Cannot create a tensor proto whose content is larger than 2GB”, leading to a final set of 1481 complexes. DSSP could only be run successfully for 1391 out of the 1481 protein complexes, and we ignored the rest in the analysis.107 of the complexes in the test set lack beta carbons (Cβs), and 50 have overlapping PDB codes with the development set and were therefore excluded. In the MSA generation from AF2, 20 MSAs report MergeMasterSlave errors regarding discrepancies in the number of match states, resulting in a total of 1484 AF2 MSAs. When folding, three of these , 34 had no matching species in the MSA pairing, 106 produced out of memory exceptions during prediction using a GPU with 40 Gb RAM, 35 gave a tensor reshape error, and 65 complexes were homodimers, leaving 1715 complexes for this set.For the mammalian proteins from Negatome, seven out of 1733 single chains were redundant according to Uniprot 27. Here, all proteins are from E. coli. Two methods were used to identify non-interacting proteins, first a set of proteins with no reported interaction signal in Yeast Two-Hybrid Experiments41 and secondly complexes whose individual proteins were found in different APMS benchmark complexes42. This dataset contains in total 3989 non-interacting pairs.Two datasets of known non-interacting proteins were used, one from the same study as the positive test set43 dataset from Negatome31. This data deemed “the manual stringent set” contains proteins annotated from the literature with experimental support describing the lack of protein interaction. Some structures in this dataset are homodimers (65) and are therefore excluded, resulting in 1705 structures. Together there are 5694 non-interacting protein complexes.The second set contains 1964 unique mammalian protein complexes filtered against the IntAct16, which builds three different MSAs generated by searching the Big Fantastic Database44 (BFD) with HHBlits34 (from hh-suite v.3.0-beta.3 version 14/07/2017) and both MGnify v.2018_1245 and Uniref90 v.2020_0146 with jackhmmer from HMMER347. The AF2 MSAs were generated by supplying a concatenated protein sequence of the entire complex to the AF2 MSA generating pipeline in FASTA format. The resulting MSAs will thus mainly contain gaps for one of the two query proteins in each row, as only single chains can obtain hits in the searched databases  consists of several MSAs. We used the AF2 MSA generation34 version 3.1.0 against uniclust30_2018_0835 with these options:In addition to the default AF2 MSA, we generated an additional MSA by simply concatenating diagonally MSAs generated independently from each of the two chains. These MSAs were constructed by running HHblitshhblits -E 0.001 -all -oa3m -n 2The concatenation is done by joining side-by-side the two input chains; then sequences from one MSA are added, aligned to the corresponding input chain. Each sequence in the MSA is then elongated with gaps (on the right side if it is the left sequence MSA or the other way around), to reach the length of the two concatenated input chains. The process is then repeated for the other input chain MSA to complete the block diagonalization.24  using the options:Unaligned FASTA sequences were extracted from the three AF2 default MSAs. Obtained sequences were processed with the CD-HIT software-c 0.62 -G 0 -n 3 -aS 0.9We calculated the Neff scores separately for paired and AF2 MSAs.16 (AF2) by modifying the script https://github.com/deepmind/alphafold/blob/main/run_alphafold.py to insert a chain break of 200 residues—as suggested in the development of RoseTTAFold17 (RF). During modelling, relaxation was turned off. We note that performing model relaxation did not increase performance in the AF2 paper16 and was, therefore, ignored to save computational cost. No templates were used to build structures, as this would not assess the prediction accuracy of unknown structures or structures without sufficient matching templates. Further, AF2 has been shown to perform well for single chains without templates and has reported higher accuracy than template-based methods even when robust templates are available16.We modelled complexes using AlphaFold252 and alignment errors16. We ran these two different models by using two different configurations. The configurations utilise a varying amount of recycles and ensemble structures. Recycle refers to the number of times iterative refinement is applied by feeding the intermediate outputs recursively back into the same neural network modules. At each recycling, the MSAs are resampled, allowing for new information to be passed through the network. The number of ensembles refers to how many times information is passed through the neural network before it is averaged16. The two configurations used are; the CASP14 configuration  and an increased number of recycles (ten) but only one ensembles.We supplied four different types of MSAs to AF2: (1) the MSAs generated by using the default AF2 settings, (2) the top paired MSAs constructed using HHblits, described above, (3) both alignments together and finally, (4) the top paired and single-chain MSAs from HHblits to speed up predictions (only for the test set). AF2 was run with two different network models, AF2 model_1 (used in CASP14) and AF2 model_1_ptm, for each MSA. The second model, model_1_ptm, is a fine-tuned version of model_1 that predicts the TMscoreSince structure prediction with AF2 is a non-deterministic process, we generate five models initiated with different seeds. To save computational cost, this was only performed for the best modelling strategy. We rank the five models for each complex by the number of residues in the interface, giving the best result.17 was run using the paired MSAs with the top hits. The RoseTTAFold pipeline for complex modelling only generates MSAs for bacterial protein complexes, while the proteins in our test set are mainly Eukaryotic. Therefore, we use the paired alignments here. We compare RF with AF2 using the same inputs (the paired MSAs) for both the development and test datasets to provide a more fair comparison, as AF2 searches many different databases to obtain as much evolutionary information as possible when generating its MSAs. To predict the complexes, we use the “chain break modelling” as suggested in RF (https://github.com/RosettaCommons/RoseTTAFold/tree/main/example/complex_modeling) using the following command:For comparison, the RoseTTAFold (RF) end-to-end versionpredict_complex.py -i msa.a3m -o complex -Ls chain1_length chain2_lengthNo optimisation of the RF protocol was made here.30 was run through the provided webserver (https://zougrouptoolkit.missouri.edu/MDockPP/). This docking algorithm is based on fast Fourier transform (FFT). The docking results are assessed using the “in-house” scoring function ITScorePP.The docking method MDockPP32, was used. Here, two protein models are docked using a FFT procedure to generate 340,000 docking poses for each complex. The bound structures extracted from complexes in the test set were used as inputs. This docking generation stage mainly considers the geometric surface properties of the two interacting structures, allowing minor clashes to leave some space for conformational flexibility adjustment. As the bound form of the proteins is used, this should represent an easy case for GRAMM-based docking, and performance drops significantly when unbound structures or models are used53. The atom-atom contact energy AACE18 is used to score and rank all poses, as this has been shown to provide better results than shape-complementarity alone54.For comparison, a rigid-body docking method, GRAMM7 referred to as “TMdock” is also adopted. The adopted template library includes 11756 protein complexes obtained from the Dockground database38 (release 28-10-2020). Monomers from target complexes are structurally aligned with complexes in the supplied libraries (depleted of the target structure PDB ID) in order to identify the best available template structure. The bound form of the template structures was used. TM scores resulting from the alignment of target proteins to each template are averaged and used to score obtained docking models. Alternatively, we refer to “TMdock Interfaces” when targets are structurally aligned only to the template interfaces, defined as every residue with a Cβ atom closer than 12 Å from any Cβ atom in the other chain.For comparison, a template-based docking protocol28, was run with the default settings. These entail creating four different MSAs. Three different MSAs are created by searching Uniref90 v.2020_0146, Uniprot v.2021_0448 and MGnify v.2018_1245 with jackhmmer from HMMER347 and one joint is created by searching the Big Fantastic Database44 (BFD) and uniclust30_2018_0835 with HHBlits34 (from hh-suite v.3.0-beta.3 version 14/07/2017).The simultaneous fold-and-dock program based on the same principles as AF2, AlphaFold-multimerThe results from the Uniprot search are used for MSA pairing and the results from the remaining searches are used to create a block-diagonalized MSA, similar to the procedures described above. All four MSAs are then used to fold a protein complex. Some complexes failed due to computational limitations, resulting in 1458 out of 1481 complexes successfully folded.33 was then run  to compare the predicted and actual interfaces. This program compares interfaces using a combination of three different CAPRI55 quality measures  converted to a continuous scale, where an acceptable model comprises a DockQ score of at least 0.23.The backbone atoms  were extracted from the predicted AF2 structures (as these are the only predicted atoms in the end-to-end version of RF). The interface scoring program DockQTo analyse the ability of AF2 to distinguish correct models as well as interacting from non-interacting proteins, we analyse the separation between acceptable and incorrect models as a function of different metrics on the development set: the number of unique interacting residues (Cβs from different chains within 8 Å from each other), the total number of interactions between Cβs from different chains (referred to as the number of interface contacts), average predicted lDDT (plDDT) score from AF2 for the interface, the minimum of the average plDDT for both chains and the average plDDT over the whole heterodimer.We use these metrics as a threshold to build a confusion matrix, where true/false positives (TP and FP respectively) are correct/incorrect docking models which places above the threshold and false/true negatives (FN and TN respectively) are correct/incorrect docking models which scores below the threshold. From the built confusion matrix, we derive the true positive rate (TPR), false positive rate (FPR) defined as:Then, we calculate TPR and FPR for each possible value assumed by the set of dockings given a single metric and plot TPR as a function of FPR in order to obtain an ROC curve. We compute the area under curve (AUC) for ROC curves obtained for each metric to compare different metrics. The AUC is defined as:The TPR and FPR for different thresholds are used to calculate the fraction of models that can be called correct out of all models and the positive predictive value (PPV). The fraction of acceptable and incorrect models are obtained by multiplying the TPR and FPR with the SR. Multiplying the FPR with the SR results in the false discovery rate (FDR) and the PPV can be calculated by dividing the fraction of acceptable models by the sum of the acceptable and incorrect models. The PPV, FDR and SR are defined as:56, to the DockQ scores using the average interface plDDT multiplied with the logarithm of the number of interface contacts, with the following sigmoidal equation:L =  0.724, x0 = 152.611, k = 0.052 and b = 0.018.As it is not only desirable to know when a model is accurate but also how accurate this model is, we developed a predicted DockQ score, pDockQ. This score is created by fitting a sigmoidal curve Fig.  using “c57, the length of the shortest chain, the number of residues in the interface and the number of contacts in the interface.To analyse the possibility of determining when AF2 can model a complex correctly, we analyse the structures and the MSAs. We investigated: the number of effective sequences (Neff), the secondary structure in the interface annotated using DSSP10 helix), 4-turn helix (α helix) and 5-turn helix (π helix)), sheet  and loop (residues which are not in any known conformation).DSSP was run on the entire complexes, and the resulting annotations were grouped into three categories; helix . The PPV is therefore the fraction of the top N DCA signals in the interface that are true contacts. The DCA signals are computed using GaussDCATo compare the computation required for each MSA, we compared the time it took to generate MSAs for three protein pairs , using either the block diagonalization or AF2 protocol. The tests were performed on a computer using 16 CPU cores from an Intel Xeon E5-2690v4.Fusing the MSAs took 3 s on average per tested complex. It took 7884 s for generating the AF2 MSAs, the single-chain searches took 338 s on average and the pairing 2 s. The pairing and fusing are thereby negligible compared to searching, resulting in a speedup of 24 times for the hhblits searches. In comparison, folding using the m1-10-1 strategy took 191 s on average for these pairs.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Fragment libraries play a key role in fragment-assembly based protein structure prediction, where protein fragments are assembled to form a complete three-dimensional structure. Rich and accurate structural information embedded in fragment libraries has not been systematically extracted and used beyond fragment assembly.To better leverage the valuable structural information for protein structure prediction, we extracted seven types of structural information from fragment libraries. We broadened the usage of such structural information by transforming fragment libraries into protein-specific potentials for gradient-descent based protein folding and encoding fragment libraries as structural features for protein property prediction.Fragment libraires improved the accuracy of protein folding and outperformed state-of-the-art algorithms with respect to predicted properties, such as torsion angles and inter-residue distances.Our work implies that the rich structural information extracted from fragment libraries can complement sequence-derived features to help protein structure prediction.The online version contains supplementary material available at 10.1186/s12859-021-04258-6. Protein structure prediction is one of the most challenging and active research fields in computational biology –3. Over β − Cβ pairwise distances and torsion angles. These approaches generally consist of two stages: in the protein property prediction stage, many types of structural properties are predicted for a protein sequence; in the gradient-descent based protein folding stage, structures are generated by minimizing the energy potential derived from protein properties. Given that the energy potentials are mainly derived from the predicted protein properties, the accuracy of the predicted protein properties, to a large extent, determines the quality of final predicted structures.Exemplified by AlphaFold , trRosetIn recent research and industrial pipelines of protein property prediction, such as the prediction of secondary structures , 17, torIn this study, to leverage the structural information provided by fragment libraries, we first directly extracted multiple structural properties from fragment libraries and proposed novel fragment-level metrics for evaluation. We used DeepFragLib, the state-of-the-art fragment library construction approach when benchmarked on recent CASPs , to geneWe summarize our findings as follows. First, our comprehensive analysis of fragment libraries clearly showed their potential in facilitating protein structure-related tasks. Second, our attempt to leverage fragment libraries as potential functions in gradient descent-based protein modelling pipelines brought small improvements in TM-Score, but it helped moderately in quality assessment. Finally, our proposed FA-DNN that employs fragment libraries as inputs showed superior prediction performance for torsion angles, backbone angles and Cβ distances when compared to state-of-the-art protein property predictors on four independent test sets.Initially designed to serve in fragment assembly-based simulations, fragment libraries are lists of short template structures  that are considered resemble to continuous regions of the structure of a target protein. Although a fragment library consists of thousands of fragments and thus contains rich structural information about the target protein, such information is lack of exploration and seldom used beyond fragment assembly. To obtain a quantitative understanding of structural information embedded in fragment libraries, we constructed fragment libraries for proteins in all three independent test datasets, CASP13 FM, CASP13 TBM and CAMEO  using thα − Cα and Cβ − Cβ pairwise distances (see Methods for more details). These fragment-level metrics were defined as the expectations of errors (or accuracy for fragment secondary structure) of all positions of a target protein, where the expectation for each position was defined as errors (or accuracies) of structural properties of all fragments at this position. Therefore, different from previous metrics for protein property evaluation , which were directly defined on a single residue and evaluated for the target protein, our proposed metrics are all defined on the fragment level and thus can better evaluate fragment libraries constructed by different algorithms. As shown in Fig. ϕ. These results demonstrate that fragment libraries provide rich and high-quality structural information that is lack of evaluation before. To get more accurate structural information for protein structure prediction, we further assigned a confidence score for each fragment in DeepFragLib according to the predicted RMSD value using a softmax function with T = 0.1 and constructed weighted fragment libraries (see Methods for more details). As shown in Additional file Although precision and coverage can be used to evaluate fragment libraries on an overall level, they generally fail to reflect the accuracy of detailed structural properties of a fragment library. To quantitively analyze the structural information of fragment libraries, we devised seven novel fragment-level metrics for corresponding structural properties, namely the accuracy of fragment secondary structure, the error of two torsion angles, ϕ and ψ, the error of two backbone angles θ and τ  and the Gradient-descent based protein folding is an approach that predicts protein 3D structures by directly minimizing protein property constraints. The quality of protein property constraints usually determines the accuracy of predicted structures. Considering that most constraints including inter-residue pairwise distances and torsion angles , 14 are α − Cα and Cβ − Cβ). A wGMM model was built for each property and each position of the target protein, respectively. To choose the number of components in each wGMM model, we fitted wGMMs on the torsion angle ϕ using a series of numbers of components ranging from 2 to 30 and used the averaged BIC (Bayesian Information Criterion) score as the criterion to evaluate on CASP12FM dataset. As shown in Additional file In this approach, six kinds of structural properties were extracted from fragment libraries, including two torsion angles (ϕ and ψ), two backbone angles (θ and τ) and two pairwise distances within a fragment . We first built a dataset called HR5916 by culling 5916 high-quality chains from PDB using PISCES [β − Cβ distance prediction.Besides directly utilizing fragment libraries to facilitate protein structure prediction, the structural information extracted from fragment libraries can also serve as features for protein property prediction. For this purpose, we designed FA-DNN , a deep neural network consisting of a fragment library encoder module and a structural property predictor module . As shown in Fig. β − Cβ distances with a large margin.We further compared the performance of FA-DNN and state-of-the-art algorithms for protein property prediction. As shown in Table We further made a point-to-point comparison between our FA-DNN with SPOT-1D and trRosetta on the latest CASP14 FM test set. As is shown in Fig. This work, to our knowledge, is the first attempt to exploit structural information from high-quality fragment libraries for both protein property prediction and gradient-descent based protein folding. By explicitly extracting structural information from fragment libraries and designing corresponding potentials with wGMM models, we demonstrate that leveraging fragment libraries leads to performance gains for protein structure prediction. Therefore, it might be of interest to find out what leads to the improvements. One obvious observation is that fragment libraries provide rich structural information which works as constraints beyond those predicted from sequential information. A fragment library consists of many fragments for each position of the target protein, where each fragment is a local 3D structure, containing almost all kinds of structural properties like a protein can have. Different from constraints predicted from sequence-based information, such as pairwise inter-residue distances, which mainly provide global constraints among residues, fragments capture short-ranged and local structural information. Therefore, constraints extracted from fragment libraries and those predicted from sequence-derived information, are complementary to each other. Nonetheless, we found that such local structural information can only bring limited improvement for protein folding and the gap to the best approach in the CASP competitions is still large. In this study, due to the limited number of fragments, the wGMM models were built for each kind of protein property and for each position of a target protein respectively. Taking into consideration the interactions among different protein properties and different positions, a potentially better way is to employ multivariate wGMMs to model such properties and thus produce more accurate potentials for the subsequent gradient-based protein folding.With its information implicitly extracted by a neural network, fragment library also makes a difference in protein property prediction. Considering the quality of fragment libraries for different proteins varies from one to another, we further estimated the relationship between the quality of fragment libraries and the performance gains of protein property prediction. As shown in Additional file Although both the fragment derived potentials and FA-DNN with fragment libraries as inputs showed effectiveness on their own, how to fully utilize the advantages from the two aspects and avoid redundancies still requires further study. An end-to-end model that integrates the two approaches in a single deep neural network and simultaneously optimizes predicted properties and 3D structures, can be expected in the future.To evaluate the performance of fragment libraries, we adopted three independent test sets, including 31 targets in CASP13FM   with chaWe built fragment libraries for all targets in CASP13FM, CASP13TBM and CAMEO test sets by DeepFragLib v1.0 . 50–200 α − Cα and Cβ − Cβ pairwise distances. Though these properties were originally defined on the residue level, we proposed novel metrics describing the accuracy of these properties on the fragment level.We first assessed the performance of fragment libraries constructed by different algorithms with two classical evaluation metrics, namely precision and coverage. Precision is the proportion of good fragments in the whole fragment library and coverage is the proportion of positions in each protein which are spanned by at least one good fragment. A good fragment is defined as a fragment with an RMSD smaller than a given threshold. Furthermore, in order to make a comprehensive evaluation on the structural properties of fragment libraries, we took seven protein structure properties into consideration, namely the 1D properties including the secondary structures, the torsion angles ϕ, ψ, the backbone angles θ, i.e., the planar angle between three successive Cα atoms  denotes the fragment secondary structure of fragment f. Thus, the accuracy of fragment secondary structure of the whole fragment library ACCSS(FL) is defined as the expectation of the accuracy of each position, which is then defined as the expectation of the accuracy of all fragments at this position. Similar to the above definition, we further proposed the accuracy of angles  as follows,ang denotes the mean absolute error of the angle of the fragment i. The angle error of a fragment library is defined as the expectation of those of all positions, where the angle error of a position is then defined as the expectation of those of all fragments at this position. Finally, we proposed the accuracy of two 2D properties (Cα − Cα and Cβ − Cβ pairwise distances) as follows,dist denotes the mean absolute error (MAE) between pairwise Cα − Cα or Cβ − Cβ distances within a fragment fi compared with the native structure f*. With these novel metrics, we made a comprehensive evaluation of fragment libraries constructed by NNMake, Flib-Coevo and DeepFragLib on three independent test sets.Similar to previous research, the secondary structures of fragments are divided into four classes: mainly helix (termed as H), mainly strand (termed as E), mainly coil (termed as C) and others (termed as O). A fragment is defined as H or E or C if more than half residues of the fragment are assigned with the corresponding secondary structures. Otherwise, the secondary structure of the fragment is defined as O. The accuracy of fragment secondary structure on the fragment level is defined as follows,α − Cα and Cβ − Cβ atoms were extracted from the fragment library. Considering that fragments have variable lengths, we designed a “smoothing” operation which cuts fragments into a series of 7-residue long fragments by a sliding window. This operation results in all fragments having the same length of 7 residues. We employed Gaussian mixture models to delineate the distribution of these properties for each position. Considering that each fragment recruited by DeepFragLib has a predicted RMSD, we regarded this value as a confidence score for the fragment and assigned a weight according to all fragments at the same position as follows,i denotes a fragment in F, predRMSDi denotes the predicted RMSD value of fragment fi and T is the temperature (0.1 is used throughout the experiments).As illustrated above, we extracted and analyzed structural information embedded in fragment libraries. To further exploit the rich information from fragment libraries for gradient-descent based protein folding, we built a series of models to make an explicit representation of fragment libraries and then utilized these models to design protein-specific potentials in a differentiable way. As shown in Fig. 2 denotes the weighted variance. We then built weighted Gaussian mixture (wGMM) models of each property for each residue with four components  are defined as follows,β − Cβ distances, x denotes a predicted protein structure, K is the number of components in the wGMM model, w, µ and σ are the fitted parameters of each component of the wGMM model, ϕi is the ϕ angle at the i-th residue in x and β atom of the j1 residue and the Cβ atom of the j2 residue in x. Potentials for other properties are defined in a similar way which led to a total of six potential functions (one for each property).wGMM models were then converted to potentials using a negative log likelihood function. Notably, fragment-derived potentials are customized for each protein owing to protein-specific wGMM models. For example, the protein-specific loss function of ϕ angles (1D property) and inter-residue Cβ − Cβ pairwise distances predicted by trRosetta [FL(x) as follows,FL(x) is defined as the weighted sum of the six potentials and w denotes the weight of a potential for each property. The combined potential was calculated and then minimized to update the protein structure during each step of the gradient descent process. All weights in Eq. SAMF is a gradient descent-based protein folding framework that folds protein structures in a self-adaptive way . To evalrRosetta  as proteTo facilitate protein property prediction, we designed FA-DNN, a deep neural network featuring a fragment library encoder that implicitly extracts high-level representations of a fragment library and encodes it into an embedding vector. The fragment library encoder is connected with a protein property predictor which predicts multiple protein structural properties  of homologous proteins detected by DeepMSA  and the β − Cβ distances respectively by a full-connected layer. To favor the gradient for residue pairs with short distances, we employed a distance mapping function similar to [i,j denotes the true distance between two atoms, Both the protein property predictor with inputs from the fragment library encoder and the baseline model share the same backbone architecture, i.e., a 2D residual neural network with 30 residual blocks and each residual block consisting of two convolutional layers with 64 filters, 3 × 3 kernel size and ELU activations. To prevent overfitting, a dropout rate of 0.15  was usedmilar to  as folloAdditional file 1. Supplementary Method, Figure S1–S9 and Table S1–S8.


Heme is an active center in many proteins. Here we explore computationally the role of heme in protein folding and protein structure. We model heme proteins using a hybrid model employing the AWSEM Hamiltonian, a coarse-grained forcefield for the protein chain along with AMBER, an all-atom forcefield for the heme. We carefully designed transferable force fields that model the interactions between the protein and the heme. The types of protein–ligand interactions in the hybrid model include thioester covalent bonds, coordinated covalent bonds, hydrogen bonds, and electrostatics. We explore the influence of different types of hemes (heme b and heme c) on folding and structure prediction. Including both types of heme improves the quality of protein structure predictions. The free energy landscape shows that both types of heme can act as nucleation sites for protein folding and stabilize the protein folded state. In binding the heme, coordinated covalent bonds and thioester covalent bonds for heme c drive the heme toward the native pocket. The electrostatics also facilitates the search for the binding site.The online version contains supplementary material available at 10.1007/s10867-021-09596-3. Biological physics owes a lot to the heme molecule. In the nineteenth century, Stokes, of hydrodynamics fame , noticedProteins with heme groups (iron-protoporphyrin) have been studied widely for many years –12. HemeThe functional versatility of heme proteins is partly delivered by the diversity of the types of heme groups and heme-binding sites but protein dynamics modulates, the solvent accessibility of heme, the access of axial ligands to iron, as well as the distribution of hydrophilic groups around the heme important in electron transfer , 18, 19.While some research about heme proteins concentrates on understanding the relationship between protein structure and biological function –23, otheA wide variety of protein models that depict the protein dynamics in the native state have been developed. All-atom models such as the “CHARMM” and “AMBER” forcefields have been employed to study the interactions of proteins with cofactor ligands –30. We uIn the present paper, the AWSEM-heme model is knowledge-based and transferrable. The protein is represented by the AWSEM forcefield and the heme is represented by the “AMBER” forcefield. We have considered different molecular interactions between protein and heme in this model, such as electrostatics, hydrogen bonds, and coordinate covalent bonds. Moreover, the thioester covalent bonds are included in the heme c forcefield. To understand the effects of incorporating heme in determining structure, we compared the predicted structures of heme proteins both with and without the heme molecule using AWSEM alone and the AWSEM-heme model. The heme acts to improve the quality of protein structure predictions only when the prediction of the apo form has reached sufficient accuracy. In studying the heme in the heme-binding process, we have compared several different landscapes which suggests that both the thioester covalent bonds in heme c and coordinated covalent bonds of both hemes drive the heme toward the native pocket. In the protein folding process, heme c stabilizes the folded state, which agrees with previous results with pure structure-based models .We combine the coarse-grained AWSEM  forcefield for proteins with the fully atomistic AMBER forcefield for heme to investigate protein-heme interactions. AWSEM is a predictive, coarse-grained protein folding model that has been used successfully to explore the folding, binding and misfolding of proteins. The forcefield is transferable and parameterized using energy landscape theory optimization from structural information on folded proteins . The Hamu et al. , 47. Herh et al. . They oph et al. . We usedh et al. . Besides.’s work . \documeN is the number of contacts between the heme and the protein in the predicted structure, where the contacts should also exist in the crystal structure. The Qc value is normalized so as to vary between 0 and 1.Two metrics were used to evaluate the accuracy of predicted structures when compared to crystal structures from different levels. From the protein level, we used We first set up simulations for heme b proteins and another series of simulations for heme c proteins for each one, a series using a single memory and another series that employed fragment memories. To understand the role of heme in structural prediction, we also ran protein simulations without any heme for comparison. All predicted simulations used an annealing protocol ranging down in temperature from 800K to 200K in 4 million steps Below is the link to the electronic supplementary material.


Protein function prediction is an important part of bioinformatics and genomics studies. There are many different predictors available, however most of these are in the form of web-servers instead of open-source locally installable versions. Such local versions are necessary to perform large scale genomics studies due to the presence of limitations imposed by web servers such as queues, prediction speed, and updatability of databases.This paper describes Wei2GO: a weighted sequence similarity and python-based open-source protein function prediction software. It uses DIAMOND and HMMScan sequence alignment searches against the UniProtKB and Pfam databases respectively, transfers Gene Ontology terms from the reference protein to the query protein, and uses a weighing algorithm to calculate a score for the Gene Ontology annotations.max scores, and Smin scores for biological process and molecular function ontologies. Computational time compared to Argot2 and Argot2.5 is decreased from several hours to several minutes.Wei2GO is compared against the Argot2 and Argot2.5 web servers, which use a similar concept, and DeepGOPlus which acts as a reference. Wei2GO shows an increase in performance according to precision and recall curves, Fhttps://gitlab.com/mreijnders/Wei2GO.Wei2GO is written in Python 3, and can be found at  Predicting proteins functions computationally is an integral part of omics research, due to the laborious process of experimentally determining protein functions. This is most apparent in the size difference of Uniprot’s TrEMBL and SwissProt databases, which contain predicted and manually curated proteins respectively . BecauseA comprehensive overview of protein function prediction methods is the Critical Assessment of Functional Annotation (CAFA) competition . In its e.g. by utilizing a meta-predictor  database  for UniPThe omission of a taxonomy filter used by Argot2.5.The omission of supplementing Pfam sequence similarity hits with GO terms of non-Pfam origin.The addition of a weight multiplier for GO terms derived from sequence similarity with UniProtKB proteins if these original terms are annotated with an evidence code other than IEA .Equations outlined below are in agreement with those of Argot2 and Argot2.5 unless otherwise specified. The main algorithmic differences between Argot2, Argot2.5, and Wei2GO are:Diamond or BLASTP hits against the UniProtKB database in tabular format (--outfmt 6).HMMScan hits against the Pfam-A database as a table output (--tblout).Wei2GO requires two input files:via its Snakemake pipeline  by taking the sum of the -log10 e-value E of all UniProtKB and Pfam sequence similarity hits j associated with this GO term, where e-values above 1 are filtered out:For every protein Hits is the set of BLAST and HMMScan sequence aligment hits to v annotated with GO term i, and j. Unique to Wei2GO compared to Argot is that terms annotated to a UniprotKB protein with an evidence code other than IEA  are given a weight multiplier:where P is the ratio between the number of GO terms annotated with an IEA vs GO terms annotated with an other evidence code in the GOA database. Given that the amount of GO terms annotated with an IEA code is substantially higher than those with non-IEA codes, P is larger than 1 and results in higher weights when based on non-IEA instead of IEA evidence codes.where i.e. annotations that are more generic and less prone to errors.In accordance with the GO Directed Acyclic Graph (DAG) structure all weights associated to child terms of a GO term are added to its weight, with the rationale that if a protein is annotated with a certain GO term it by default includes all its parent terms. This has the effect of assigning more confidence in GO terms higher up the DAG, The total weight associated with each protein-GO term pair is used to calculate the Internal Confidence (InC) score by taking the relative weight of the GO term compared to the weight of the root node i associated to a protein, by taking the sum of the InC of all GO terms j annotated to a protein v, if GO term j has a semantic similarity of 0.7 or higher with GO term i:The InC score is used to calculate a Group Score (GS) for each GO term I(.) is the indicator function which is equal to 1 if its argument is true and 0 otherwise, and annotations(v) is the set of GO annotations for protein v. Note that each GO term can have a unique GS score, as opposed to Argot where one singular GS score is defined for a group of similar GO terms. The value of Sim (semantic similarity) is calculated using Lin’s formula  is calculated as:where P(i) is the fraction of proteins annotated to term i. Finally, a total score (TS) for each GO term annotated to a protein v is calculated as:where i.e. i.e. a lower IC. This is done in an attempt to reduce bias towards predicting GO terms that are generic at the cost of GO terms that are highly specific, as reflected by their IC. Wei2GO instead operates under the assumption that GO terms which are easier to predict due to any reason, including having a lower IC, should have this reflected in their score. Any possible negative effects of this bias towards terms with lower IC affecting terms with higher IC will be reflected in the ‘Smin’ evaluation metric, which measures performance proportional to these IC’s .The TS score is similar to that of Argot2 and Argot2.5, which combines the scores calculated by the equations above. However, Argot introduces an additional non-cumulative InC score, GO:0005515’ (protein binding) were removed from the annotation set if it was the only molecular function term annotated to the protein, to remove a large amount of bias due to many proteins having this generic term as their only molecular function term. This same approach was used in the CAFA3 test set generation.To assess the performance of Wei2GO it is compared to Argot2.5 and its predecessor Argot2, with the addition of DeepGOPlus  which acThe test set was expanded by including all terms according to their GO DAG structure using the ‘is_a’ and ‘part_of’ relationships for parent terms. The final test set consists of 4,067 proteins with 97,054 assigned GO terms of which 1,922 proteins have 59,697 biological process terms, 651 proteins have 4,785 molecular function terms, and 2,387 proteins have 32,572 cellular component terms.t, the precision pr and recall rc are calculated per protein v, v, v, m(t) is the number of proteins with at least one GO term predicted with a score above the threshold, and n is the total number of proteins:Precision and recall is used for the assessment of Wei2GO. For a threshold The optimal balance between precision and recall is calculated using the maximum F1-score, which for this study is calculated by calculating F1-scores with 0.01 step-sizes after transforming the Total Scores to a 0–1 range:Smin score is calculated based on the remaining uncertainty ru and missing information mi, which is the IC of all false negatives and false positives, respectively. A threshold t step-size of 0.01 is used after transforming the Total Scores to a 0–1 range.The ru as:with mi as:and UniProt Knowledgebase release 2016-11 for DIAMOND searchesPfam-A release 30 for HMMER searchesDIAMOND version 0.9.11.112HMMER version 3.3GOA UniProt data release 161 for creating the Wei2GO required data filesGOA UniProt data release of 13-06-2020 for generating the test setgo.obo release 02-12-2016pfam2go generated based on interpro2go release 31-10-2016Argot2 and Argot2.5 were last accessed on 08-04-2021https://github.com/bio-ontology-research-group/deepgoplus on 30-10-2020.DeepGOPlus was downloaded from The following datasets and software versions were used:All datasets used by Wei2GO correspond to the datasets used by Argot2 and Argot2.5.Wei2GO is made to facilitate open-source protein function prediction, and the ability to do this independent of web-servers. Two of these web-servers, Argot2.5 and its predecessor Argot2, utilize sequence similarity searches against the UniProtKB and Pfam databases, and apply a weighing algorithm to come to a final prediction score. Wei2GO takes that idea and puts this in an open-source and locally installable concept, which opens up the possibility to update data used by the algorithm independent of the original authors, and the ability to perform protein function prediction on big datasets.Because Wei2GO’s concept is based on that of Argot, it is important to highlight major algorithmic differences between the different software . Both ArTo get an indication of the effect of the algorithmic differences between the software, max scores, Smin scores, and computational time. DeepGOPlus is added as a part of all these analyses. This deep learning-based and open source protein function prediction software is an excellent example of both predictive performance and usability. Adding this software in the assessments provides perspective as to any performance differences between Wei2GO and Argot. For the purpose of this analysis, all databases used by Wei2GO are the same versions used by Argot2 and Argot2.5. Each species was given to Argot2.5 as a separate input with a species level taxonomic code.Several performance metrics are used to compare Wei2GO and Argot: precision-recall curves, FPrecision recall curves were generated for biological process (BPO), molecular function (MFO), and cellular component (CCO) ontologies . This anmax scores were calculated based on the highest F1-score at any given threshold, and Smin scores were calculated based on the lowest S-score at any given threshold  and minimum S-scores (Smin) between Wei2GO only using DIAMOND input, only using HMMScan input, and using both as an input, for biological processes (BPO), molecular functions (MFO), and cellular components (CCO).Click here for additional data file.10.7717/peerj.12931/supp-2Supplemental Information 2Compared are Wei2GO with both DIAMOND and HMMScan as an input, Wei2GO with only DIAMOND as an input, and Wei2GO with only HMMScan as an input.Click here for additional data file.