ProfNet, a method to derive profile-profile alignment scoring functions that improves the alignments of distantly related proteins

BACKGROUND: Profile-profile methods have been used for some years now to detect and align homologous proteins. The best such methods use information from the background distribution of amino acids and substitution tables either when constructing the profiles or in the scoring. This makes the methods dependent on the quality and choice of substitution table as well as the construction of the profiles. Here, we introduce a novel method called ProfNet that is used to derive a profile-profile scoring function. The method optimizes the discrimination between scores of related and unrelated residues and it is fast and straightforward to use. This new method derives a scoring function that is mainly dependent on the actual alignment of residues from a training set, and it does not use any additional information about the background distribution. RESULTS: It is shown that ProfNet improves the discrimination of related and unrelated residues. Further it can be used to improve the alignment of distantly related proteins. CONCLUSION: The best performance is obtained using superfamily related proteins in the training of ProfNet, and a classifier that is related to the distance between the structurally aligned residues. The main difference between the new scoring function and a traditional profile-profile scoring function is that conserved residues on average score higher with the new function

Prediction of MHC class I binding peptides, using SVMHC

BACKGROUND: T-cells are key players in regulating a specific immune response. Activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules. MHC-peptide complexes are potential tools for diagnosis and treatment of pathogens and cancer, as well as for the development of peptide vaccines. Only one in 100 to 200 potential binders actually binds to a certain MHC molecule, therefore a good prediction method for MHC class I binding peptides can reduce the number of candidate binders that need to be synthesized and tested. RESULTS: Here, we present a novel approach, SVMHC, based on support vector machines to predict the binding of peptides to MHC class I molecules. This method seems to perform slightly better than two profile based methods, SYFPEITHI and HLA_BIND. The implementation of SVMHC is quite simple and does not involve any manual steps, therefore as more data become available it is trivial to provide prediction for more MHC types. SVMHC currently contains prediction for 26 MHC class I types from the MHCPEP database or alternatively 6 MHC class I types from the higher quality SYFPEITHI database. The prediction models for these MHC types are implemented in a public web service available at http://www.sbc.su.se/svmhc/. CONCLUSIONS: Prediction of MHC class I binding peptides using Support Vector Machines, shows high performance and is easy to apply to a large number of MHC class I types. As more peptide data are put into MHC databases, SVMHC can easily be updated to give prediction for additional MHC class I types. We suggest that the number of binding peptides needed for SVM training is at least 20 sequences

A new census of protein tandem repeats and their relationship with intrinsic disorder

Protein tandem repeats (TRs) are often associated with immunity-related functions and diseases. Since that last census of protein TRs in 1999, the number of curated proteins increased more than seven-fold and new TR prediction methods were published. TRs appear to be enriched with intrinsic disorder and vice versa. The significance and the biological reasons for this association are unknown. Here, we characterize protein TRs across all kingdoms of life and their overlap with intrinsic disorder in unprecedented detail. Using state-of-the-art prediction methods, we estimate that 50.9% of proteins contain at least one TR, often located at the sequence flanks. Positive linear correlation between the proportion of TRs and the protein length was observed universally, with Eukaryotes in general having more TRs, but when the difference in length is taken into account the difference is quite small. TRs were enriched with disorder-promoting amino acids and were inside intrinsically disordered regions. Many such TRs were homorepeats. Our results support that TRs mostly originate by duplication and are involved in essential functions such as transcription processes, structural organization, electron transport and iron-binding. In viruses, TRs are found in proteins essential for virulence

Preferential attachment in the evolution of metabolic networks

BACKGROUND: Many biological networks show some characteristics of scale-free networks. Scale-free networks can evolve through preferential attachment where new nodes are preferentially attached to well connected nodes. In networks which have evolved through preferential attachment older nodes should have a higher average connectivity than younger nodes. Here we have investigated preferential attachment in the context of metabolic networks. RESULTS: The connectivities of the enzymes in the metabolic network of Escherichia coli were determined and representatives for these enzymes were located in 11 eukaryotes, 17 archaea and 46 bacteria. E. coli enzymes which have representatives in eukaryotes have a higher average connectivity while enzymes which are represented only in the prokaryotes, and especially the enzymes only present in βγ-proteobacteria, have lower connectivities than expected by chance. Interestingly, the enzymes which have been proposed as candidates for horizontal gene transfer have a higher average connectivity than the other enzymes. Furthermore, It was found that new edges are added to the highly connected enzymes at a faster rate than to enzymes with low connectivities which is consistent with preferential attachment. CONCLUSION: Here, we have found indications of preferential attachment in the metabolic network of E. coli. A possible biological explanation for preferential attachment growth of metabolic networks is that novel enzymes created through gene duplication maintain some of the compounds involved in the original reaction, throughout its future evolution. In addition, we found that enzymes which are candidates for horizontal gene transfer have a higher average connectivity than other enzymes. This indicates that while new enzymes are attached preferentially to highly connected enzymes, these highly connected enzymes have sometimes been introduced into the E. coli genome by horizontal gene transfer. We speculate that E. coli has adjusted its metabolic network to a changing environment by replacing the relatively central enzymes for better adapted orthologs from other prokaryotic species

MPRAP: An accessibility predictor for a-helical transmem-brane proteins that performs well inside and outside the membrane

<p>Abstract</p> <p>Background</p> <p>In water-soluble proteins it is energetically favorable to bury hydrophobic residues and to expose polar and charged residues. In contrast to water soluble proteins, transmembrane proteins face three distinct environments; a hydrophobic lipid environment inside the membrane, a hydrophilic water environment outside the membrane and an interface region rich in phospholipid head-groups. Therefore, it is energetically favorable for transmembrane proteins to expose different types of residues in the different regions.</p> <p>Results</p> <p>Investigations of a set of structurally determined transmembrane proteins showed that the composition of solvent exposed residues differs significantly inside and outside the membrane. In contrast, residues buried within the interior of a protein show a much smaller difference. However, in all regions exposed residues are less conserved than buried residues. Further, we found that current state-of-the-art predictors for surface area are optimized for one of the regions and perform badly in the other regions. To circumvent this limitation we developed a new predictor, MPRAP, that performs well in all regions. In addition, MPRAP performs better on complete membrane proteins than a combination of specialized predictors and acceptably on water-soluble proteins. A web-server of MPRAP is available at <url>http://mprap.cbr.su.se/</url></p> <p>Conclusion</p> <p>By including complete <it>a</it>-helical transmembrane proteins in the training MPRAP is able to predict surface accessibility accurately both inside and outside the membrane. This predictor can aid in the prediction of 3D-structure, and in the identification of erroneous protein structures.</p

Pcons.net: protein structure prediction meta server

The Pcons.net Meta Server (http://pcons.net) provides improved automated tools for protein structure prediction and analysis using consensus. It essentially implements all the steps necessary to produce a high quality model of a protein. The whole process is fully automated and a potential user only submits the protein sequence. For PSI-BLAST detectable targets, an accurate model is generated within minutes of submission. For more difficult targets the sequence is automatically submitted to publicly available fold-recognition servers that use more advanced approaches to find distant structural homologs. The results from these servers are analyzed and assessed for structural correctness using Pcons and ProQ; and the user is presented with a ranked list of possible models. In addition, if the protein sequence contains more than one domain, these are automatically parsed out and resubmitted to the server as individual queries

Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes

Motivation Despite near-experimental accuracy on single-chain predictions, there is still scope for improvement among multimeric predictions. Methods like AlphaFold-Multimer and FoldDock can accurately model dimers. However, how well these methods fare on larger complexes is still unclear. Further, evaluation methods of the quality of multimeric complexes are not well established. Results We analysed the performance of AlphaFold-Multimer on a homology-reduced dataset of homo- and heteromeric protein complexes. We highlight the differences between the pairwise and multi-interface evaluation of chains within a multimer. We describe why certain complexes perform well on one metric (e.g. TM-score) but poorly on another (e.g. DockQ). We propose a new score, Predicted DockQ version 2 (pDockQ2), to estimate the quality of each interface in a multimer. Finally, we modelled protein complexes (from CORUM) and identified two highly confident structures that do not have sequence homology to any existing structures. Availability and implementation All scripts, models, and data used to perform the analysis in this study are freely available at https://gitlab.com/ElofssonLab/afm-benchmark