Disordered proline-rich motifs are common across the proteomes of many species and are often involved in protein-protein interactions. Proline is a unique amino acid due to the covalent bond between the backbone nitrogen and the proline side chain. The resulting five-membered ring allows proline to sample the cis state about its peptide bond, which other residues cannot do as readily. Because proline-rich disordered sequences exist as ensembles that likely include structures with the proline peptide bond in cis , a robust methodology to accurately account for these conformations in the overall ensemble is crucial. Observing the cis conformations of proline in a disordered sequence is challenging both experimentally and computationally. Nitrogen-hydrogen NMR spectroscopy cannot directly observe proline residues, which lack an amide bond, and computational methods struggle to overcome the large kinetic barrier between the cis and trans states, since isomerization usually occurs on the order of seconds. In the current work, Gaussian accelerated molecular dynamics was used to overcome this free energy barrier and simulate proline isomerization in a tetrapeptide (KPTP) and in the 12-residue proline-rich SH3 binding peptide, ArkA. We found that Gaussian accelerated molecular dynamics, when combined with a lowered peptide bond dihedral angle potential energy barrier (15 kcal/mol), allowed sufficient sampling of the proline cis and trans states on a microsecond timescale. All ArkA prolines spend a significant fraction of time in cis , leading to a more compact ensemble with less polyproline II helix structure than an ArkA ensemble with all peptide bonds in trans . The ensemble containing cis prolines also matches more closely to in vitro circular dichroism data than the all- trans ensemble. The ability of the ArkA prolines to isomerize likely affects the peptide’s ability to bind its partner SH3 domain, and should be studied further. This is the first molecular dynamics simulation study of proline isomerization in a biologically relevant proline-rich sequence that we know of, and a similar protocol could be applied to study multi-proline isomerization in other proline-containing proteins to improve conformational diversity and agreement with in vitro data.
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Entropy, Fluctuations, and Disordered Proteins
Entropy should directly reflect the extent of disorder in proteins. By clustering structurally related proteins and studying the multiple-sequence-alignment of the sequences of these clusters, we were able to link between sequence, structure, and disorder information. We introduced several parameters as measures of fluctuations at a given MSA site and used these as representative of the sequence and structure entropy at that site. In general, we found a tendency for negative correlations between disorder and structure, and significant positive correlations between disorder and the fluctuations in the system. We also found evidence for residue-type conservation for those residues proximate to potentially disordered sites. Mutation at the disorder site itself appear to be allowed. In addition, we found positive correlation for disorder and accessible surface area, validating that disordered residues occur in exposed regions of proteins. Finally, we also found that fluctuations in the dihedral angles at the original mutated residue and disorder are positively correlated while dihedral angle fluctuations in spatially proximal residues are negatively correlated with disorder. Our results seem to indicate permissible variability in the disordered site, but greater rigidity in the parts of the protein with which the disordered site interacts. This is another indication that disordered residues are involved in protein function.
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- Award ID(s):
- 1661391
- NSF-PAR ID:
- 10172221
- Date Published:
- Journal Name:
- Entropy
- Volume:
- 21
- Issue:
- 8
- ISSN:
- 1099-4300
- Page Range / eLocation ID:
- 764
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Intrinsically disordered regions lack stable structure in their native conformation but are nevertheless functional and highly abundant, particularly in Eukaryotes. Disordered moonlighting regions (DMRs) are intrinsically disordered regions that carry out multiple functions. DMRs are different from moonlighting proteins that could be structured and that are annotated at the whole‐protein level. DMRs cannot be identified by current predictors of functions of disorder that focus on specific functions rather than multifunctional regions. We conceptualized, designed and empirically assessed first‐of‐its‐kind sequence‐based predictor of DMRs, DMRpred. This computational tool outputs propensity for being in a DMR for each residue in an input protein sequence. We developed novel amino acid indices that quantify propensities for functions relevant to DMRs and used evolutionary conservation, putative solvent accessibility and intrinsic disorder derived from the input sequence to build a rich profile that is suitable to accurately predict DMRs. We processed this profile to derive innovative features that we input into a Random Forest model to generate the predictions. Empirical assessment shows that DMRpred generates accurate predictions with area under receiver operating characteristic curve = 0.86 and accuracy = 82%. These results are significantly better than the closest alternative approaches that rely on sequence alignment, evolutionary conservation and putative disorder and disorder functions. Analysis of abundance of putative DMRs in the human proteome reveals that as many as 25% of proteins may have long >30 residues) DMRs. A webserver implementation of DMRpred is available at
http://biomine.cs.vcu.edu/servers/DMRpred/ -
We propose a framework to convert the protein intrinsic disorder content to structural entropy (H) using Shannon’s information theory (IT). The structural capacity (C), which is the sum of H and structural information (I), is equal to the amino acid sequence length of the protein. The structural entropy of the residues expands a continuous spectrum, ranging from 0 (fully ordered) to 1 (fully disordered), consistent with Shannon’s IT, which scores the fully-determined state 0 and the fully-uncertain state 1. The intrinsically disordered proteins (IDPs) in a living cell may participate in maintaining the high-energy-low-entropy state. In addition, under this framework, the biological functions performed by proteins and associated with the order or disorder of their 3D structures could be explained in terms of information-gains or entropy-losses, or the reverse processes.more » « less
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We have investigated the structural evolution in solutions of the intrinsically disordered protein, α-synuclein, as a function of protein concentration and added salt concentration. Accounting for electrostatic and excluded volume interactions based on the protein sequence, our Langevin dynamics simulations reveal that α-synuclein molecules assemble into aggregates and percolated structures with a spontaneous selection of a dominant structure characteristic of microphase separation. This microphase assembly is mainly driven by electrostatic interactions between the residues in N-terminal and C-terminal of the protein molecules, and presence of salt loosens the compactness of the microstructures. We have quantified the features of the spontaneously formed microstructures using interchain radial distribution functions, and experimentally measurable inter-residue contact maps and static structure factors. Our results are in contrast to the commonly hypothesized mechanism of liquid–liquid phase separation (LLPS) for the formation of droplets in solutions of intrinsically disordered proteins, opening a new paradigm to understand the birth and structure of membraneless organelles. In general, construction of phase diagrams of intrinsically disordered proteins and other biomacromolecular systems needs to incorporate features of microphase separation into other mechanisms of macrophase separation and percolation.more » « less
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Abstract Motivation The analysis of sequence conservation patterns has been widely utilized to identify functionally important (catalytic and ligand-binding) protein residues for over a half-century. Despite decades of development, on average state-of-the-art non-template-based functional residue prediction methods must predict ∼25% of a protein’s total residues to correctly identify half of the protein’s functional site residues. The overwhelming proportion of false positives results in reported ‘F-Scores’ of ∼0.3. We investigated the limits of current approaches, focusing on the so-far neglected impact of the specific choice of homologs included in multiple sequence alignments (MSAs).
Results The limits of conservation-based functional residue prediction were explored by surveying the binding sites of 1023 proteins. A straightforward conservation analysis of MSAs composed of randomly selected homologs sampled from a PSI-BLAST search achieves average F-Scores of ∼0.3, a performance matching that reported by state-of-the-art methods, which often consider additional features for the prediction in a machine learning setting. Interestingly, we found that a simple combinatorial MSA sampling algorithm will in almost every case produce an MSA with an optimal set of homologs whose conservation analysis reaches average F-Scores of ∼0.6, doubling state-of-the-art performance. We also show that this is nearly at the theoretical limit of possible performance given the agreement between different binding site definitions. Additionally, we showcase the progress in this direction made by Selection of Alignment by Maximal Mutual Information (SAMMI), an information-theory-based approach to identifying biologically informative MSAs. This work highlights the importance and the unused potential of optimally composed MSAs for conservation analysis.
Supplementary information Supplementary data are available at Bioinformatics online.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/e21080764
