| 1. |
- Duart, Gerard, et al.
(författare)
-
Intra-helical salt bridge contribution to membrane protein insertion
- 2024
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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.
|
|
| 2. |
- Duart, Gerard, et al.
(författare)
-
Intra-Helical Salt Bridge Contribution to Membrane Protein Insertion
- 2022
-
Ingår i: Journal of Molecular Biology. - : Elsevier BV. - 0022-2836 .- 1089-8638. ; 434:5
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 3. |
- Govindarajan, Sudha, et al.
(författare)
-
The evolutionary history of topological variations in the CPA/AT superfamily
- 2024
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- CPA/AT transporters consist of two structurally and evolutionarily related inverted repeat units, each of them with one core and one scaffold subdomain. During evolution, these families have undergone substantial changes in structure, topology and function. Central to the function of the transporters is the existence of two noncanonical helices that are involved in the transport process. In different families, two different types of these helices have been identified, reentrant and broken. Here, we use an integrated topology annotation method to identify novel topologies in the families. It combines topology prediction, similarity to families with known structure, and the difference in positively charged residues present in inside and outside loops in alternative topological models. We identified families with diverse topologies containing broken or reentrant helix. We classified all families based on 3 distinct evolutionary groups that each share a structurally similar C-terminal repeat unit newly termed as “Fold-types”. Using the evolutionary relationship between families we propose topological transitions including, a transition between broken and reentrant helices, complete change of orientation, changes in the number of scaffold helices and even in some rare cases, losses of core helices. The evolutionary history of the repeat units shows gene duplication and repeat shuffling events to result in these extensive topology variations. The novel structure-based classification, together with supporting structural models and other information, is presented in a searchable database, CPAfold (cpafold.bioinfo.se). Our comprehensive study of topology variations within the CPA superfamily provides better insight about their structure and evolution.
|
|
| 4. |
- Hatos, Andras, et al.
(författare)
-
DisProt : intrinsic protein disorder annotation in 2020
- 2020
-
Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 48:D1, s. D269-D276
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 5. |
- Lamb, John, et al.
(författare)
-
PconsFam : An Interactive Database of Structure Predictions of Pfam Families
- 2019
-
Ingår i: Journal of Molecular Biology. - : Elsevier BV. - 0022-2836 .- 1089-8638. ; 431:13, s. 2442-2448
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 6. |
- Lamb, John, et al.
(författare)
-
pyconsFold : A fast and easy tool for modelling and docking using distance predictions
- 2024
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Motivation Contact predictions within a protein has 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 inter-protein 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 modelling 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 inter-protein contacts 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.Contact arne{at}bioinfo.seSupplemental material Install instructions, examples and parameters can be found in the supplemental notes.Availability of data The data underlying this article together with source code are available on github, at https://github.com/johnlamb/pyconsfold.
|
|
| 7. |
- Lamb, John, et al.
(författare)
-
pyconsFold : a fast and easy tool for modeling and docking using distance predictions
- 2021
-
Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 37:21, s. 3959-3960
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 8. |
- Lamb, John, 1983-
(författare)
-
Transmembrane Proteins and Protein Structure Prediction : What we can learn from Computational Methods
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
|
|
| 9. |
- Pozzati, Gabriele, et al.
(författare)
-
Limits and potential of combined folding and docking
- 2022
-
Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 38:4, s. 954-961
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 10. |
|
|
