Salt bridges between negatively (D, E) and positively charged (K, R, H) amino acids play an important role in protein stabilization. This has a more prevalent effect in membrane proteins where polar amino acids are exposed to a hydrophobic environment. In transmembrane (TM) helices the presence of charged residues can hinder the insertion of the helices into the membrane. It is possible that the formation of salt bridges could decrease the cost of membrane integration. However, the presence of intra-helical salt bridges in TM domains and their effect on insertion has not been properly studied yet. In this work, we show that potentially salt-bridge forming pairs are statistically over-represented in TM-helices. We then selected some candidates to experimentally determine the contribution of these electrostatic interactions to the translocon-assisted membrane insertion process. Using both in vitro and whole cell systems, we confirm the presence of intra-helical salt bridges in TM segments during biogenesis and determined that they contribute ~0.5 kcal/mol to the apparent free energy of membrane insertion (delta G(app)). Our observations suggest that salt bridge interactions can be stabilized during translocon-mediated insertion and thus could be relevant to consider for the future development of membrane protein prediction software. 

Motivation: In the last decade, de novo protein structure prediction accuracy for individual proteins has improved significantly by utilising deep learning (DL) methods for harvesting the co-evolution information from large multiple sequence alignments (MSAs). The same approach can, in principle, also be used to extract information about evolutionary-based contacts across protein-protein interfaces. However, most earlier studies have not used the latest DL methods for inter-chain contact distance prediction. This article introduces a fold-and-dock method based on predicted residue-residue distances with trRosetta.

Results: The method can simultaneously predict the tertiary and quaternary structure of a protein pair, even when the structures of the monomers are not known. The straightforward application of this method to a standard dataset for protein-protein docking yielded limited success. However, using alternative methods for generating MSAs allowed us to dock accurately significantly more proteins. We also introduced a novel scoring function, PconsDock, that accurately separates 98% of correctly and incorrectly folded and docked proteins. The average performance of the method is comparable to the use of traditional, template-based or ab initio shape-complementarity-only docking methods. Moreover, the results of conventional and fold-and-dock approaches are complementary, and thus a combined docking pipeline could increase overall docking success significantly. This methodology contributed to the best model for one of the CASP14 oligomeric targets, H1065.

Motivation: Contact predictions within a protein have recently become a viable method for accurate prediction of protein structure. Using predicted distance distributions has been shown in many cases to be superior to only using a binary contact annotation. Using predicted interprotein distances has also been shown to be able to dock some protein dimers.

Results: Here, we present pyconsFold. Using CNS as its underlying folding mechanism and predicted contact distance it outperforms regular contact prediction-based modeling on our dataset of 210 proteins. It performs marginally worse than the state-of-the-art pyRosetta folding pipeline but is on average about 20 times faster per model. More importantly pyconsFold can also be used as a fold-and-dock protocol by using predicted interprotein contacts/distances to simultaneously fold and dock two protein chains.

Availability and implementation: pyconsFold is implemented in Python 3 with a strong focus on using as few dependencies as possible for longevity. It is available both as a pip package in Python 3 and as source code on GitHub and is published under the GPLv3 license. The data underlying this article together with source code are available on github, at https://github.com/johnlamb/pyconsfold.

CPA/AT transporters are made up of scaffold and a core domain. The core domain contains two non-canonical helices (broken or reentrant) that mediate the transport of ions, amino acids or other charged compounds. During evolution, these transporters have undergone substantial changes in structure, topology and function. To shed light on these structural transitions, we create models for all families using an integrated topology annotation method. We find that the CPA/AT transporters can be classified into four fold-types based on their structure; (1) the CPA-broken fold-type, (2) the CPA-reentrant fold-type, (3) the BART fold-type, and (4) a previously not described fold-type, the Reentrant-Helix-Reentrant fold-type. Several topological transitions are identified, including the transition between a broken and reentrant helix, one transition between a loop and a reentrant helix, complete changes of orientation, and changes in the number of scaffold helices. These transitions are mainly caused by gene duplication and shuffling events. Structural models, topology information and other details are presented in a searchable database, CPAfold (cpafold.bioinfo.se). Author summary The availability of experimentally solved transmembrane transport structures are sparse, and modelling is challenging as the families contain non-canonical transmembrane helices. Here, we present structural models for all families of CPA/AT transporters. These proteins are then classified into four fold-types, including one novel fold-type, the reentrant-helix-reentrant fold type. We find extensive structural variations within the fold with members having from three to fourteen transmembrane helices. We explore the evolutionary mechanisms that have shaped the topological variations providing a deeper understanding of membrane protein structure and evolution. We also believe our work could serve as a model system to understand the evolution of topology variations for other membrane proteins.

A protein’s 3D-structure is essential to understand how proteins function and interact and how biochemical processes proceed in organic life. Despite the advancement in experimental methods, it remains expensive and time-consuming to determine protein structure experimentally. There have been significant advances in machine learning and computational methods where, in many cases, models of protein structure can be determined to a high level of quality. Using computational methods helps predict protein 3D-structure and is often used complementary to experimental methods to give better insight and understanding of biological processes.

This thesis presents studies focusing on the simplicity and transparency of the 3D-structure pipeline. This is done with a new interactive database with full access to the pipeline’s data and code together with tools to analyse and compare models and structures. 

I present a new module for the last step in this pipeline, the final folding of the protein chain, which both simplifies the current pipeline and uses new input data based on the current research. This module predicts better models than its predecessor and produces models more than a magnitude faster than the current state-of-the-art tools. This module also contains a novel way of both folding and docking dimers in one single step. 

There are many examples of how machine learning models contain biases that originate in biased training data, translating into models that do not generalise well. I present a study where experts collaborate to create a high-quality database of Intrinsically Disordered Proteins. Through manual annotation and quality protocols, high-quality training data has been produced that is well suited for machine learning tasks and protein disorder analysis. In this thesis, I also present computational methods pertaining to transmembrane proteins and how they can increase our insight into membrane protein structure. In one study, we use computational methods together with experimental methods to investigate how differently charged residue pairs that form salt bridges inside the membrane of membrane proteins changes the insertion potential. We show that amino acid pairs that form salt bridges in this setting contribute 0.5-0.7 kcal/mol to membrane insertion’s apparent free energy. This gives new insight and advances in how we calculate insertion and can lead to better membrane protein topology predictors. In the final study, we investigate the CPA/AT-transporter family of transmembrane proteins and create a new integrated topology annotation method and structural classification, resulting in new insight into how this family evolved through time. The entire pipeline is published as an interactive database with complete transparency for both the method and data used. The study shows how this family has evolved by duplicating internal regions and how this has caused a structural symmetry in the family. 

This thesis, therefore, contributes to a more accessible and more transparent path of using computational methods to give a more extensive insight into protein structure prediction and how these structures pertain to biochemical processes.

The Database of Protein Disorder (DisProt, URL:https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the 'dark' proteome.

At present, about half of the protein domain families lack a structural representative. However, in the last decade, predicting contact maps and using these to model the tertiary structure for these protein families have become an alternative approach to gain structural insight. At present, reliable models for several hundreds of protein families have been created using this approach. To increase the use of this approach, we present PconsFam, which is an intuitive and interactive database for predicted contact maps and tertiary structure models of the entire Pfam database. By modeling all possible families, both with and without a representative structure, using the PconsFold2 pipeline, and running quality assessment estimator on the models, we predict an estimation for how confident the contact maps and structures are for each family.

Transmembrane proteins perform a variety of important biological functions necessary for the survival and growth of the cells. Membrane proteins are built up by transmembrane segments that span the lipid bilayer. The segments can either be in the form of hydrophobic alpha-helices or beta-sheets which create a barrel. A fundamental aspect of the structure of transmembrane proteins is the membrane topology, that is, the number of transmembrane segments, their position in the protein sequence and their orientation in the membrane. Along these lines, many predictive algorithms for the prediction of the topology of alpha-helical and beta-barrel transmembrane proteins exist. The newest algorithms obtain an accuracy close to 80% both for alpha-helical and beta-barrel transmembrane proteins. However, lately it has been shown that the simplified picture presented when describing a protein family by its topology is limited. To demonstrate this, we highlight examples where the topology is either not conserved in a protein superfamily or where the structure cannot be described solely by the topology of a protein. The prediction of these nonstandard features from sequence alone was not successful until the recent revolutionary progress in 3D-structure prediction of proteins.

Salt bridges between negatively (D, E) and positively charged (K, R, H) amino acids play an important role in protein stabilization. This has a more prevalent effect in membrane proteins where polar amino acids are exposed to a very hydrophobic environment. In transmembrane (TM) helices the presence of charged residues can hinder the insertion of the helices into the membrane. This can sometimes be avoided by TM region rearrangements after insertion, but it is also possible that the formation of salt bridges could decrease the cost of membrane integration. However, the presence of intra-helical salt bridges in TM domains and their effect on insertion has not been properly studied yet. In this work, we use an analytical pipeline to study the prevalence of charged pairs of amino acid residues in TM α-helices, which shows that potentially salt-bridge forming pairs are statistically over-represented. We then selected some candidates to experimentally determine the contribution of these electrostatic interactions to the translocon-assisted membrane insertion process. Using both in vitro and in vivo systems, we confirm the presence of intra-helical salt bridges in TM segments during biogenesis and determined that they contribute between 0.5-0.7 kcal/mol to the apparent free energy of membrane insertion (ΔGapp). Our observations suggest that salt bridge interactions can be stabilized during translocon-mediated insertion and thus could be relevant to consider for the future development of membrane protein prediction software.