Molecular Information Theory Meets Protein Folding

Sánchez, Ignacio E.; Galpern, Ezequiel A.; Garibaldi, Martín M; Ferreiro, Diego U.

doi:10.1021/acs.jpcb.2c04532

Cited by 6 publications

(5 citation statements)

References 117 publications

(294 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This value, corresponding to an average of

2^{2.6} \approx 6

possible amino acids in each position, or from

\approx 5

\approx 8

considering the predominant range of

S_{0}

values estimated in Ref. 6, turns out to be reasonably consistent with previous direct estimates, 15–17 as seen above. There would be other conformations with similar sequence‐dependent energy for these folding sequences but they are expected to have higher sequence‐independent energy, not being native‐like.…”

Section: Discussionsupporting

confidence: 89%

“…This value appears to be smaller than expected since it would correspond to an average of just two different amino acids in each position. Deep exploration of sequence space for individual structures 15,16 and recent analyses of large sets of sequence alignments 17 have both provided higher estimates that happen to be closer to the present estimate for

S^{seq} (T_{g})

than for

S^{seq} (T_{sel})

. In any case, regardless of any specific estimate, for each sequence that folds to a particular native structure, having energy close to

E_{normalN}

in that structure, there are many other sequences, corresponding to an entropy of

S^{seq} (T_{g}) - S^{seq} (T_{sel})

, that are unable to fold to that structure nor to any other structure.…”

Section: Theoretical Analysismentioning

confidence: 99%

See 1 more Smart Citation

Sequence‐dependent and ‐independent information in a combined random energy model for protein folding and coding

Pereira de Araújo

2023

Proteins

View full text Add to dashboard Cite

Random energy models (REMs) provide a simple description of the energy landscapes that guide protein folding and evolution. The requirement of a large energy gap between the native structure and unfolded conformations, considered necessary for cooperative, protein‐like, folding behavior, indicates that proteins differ markedly from random heteropolymers. It has been suggested, therefore, that natural selection might have acted to choose nonrandom amino acid sequences satisfying this particular condition, implying that a large fraction of possible, unselected random sequences, would not fold to any structure. From an informational perspective, however, this scenario could indicate that protein structures, regarded as messages to be transmitted through a communication channel, would not be efficiently encoded in amino acid sequences, regarded as the communication channel for this transmission, since a large fraction of possible channel states would not be used. Here, we use a combined REM for conformations and sequences, with previously estimated parameters for natural proteins, to explore an alternative possibility in which the appropriate shape of the landscape results mainly from the deviation from randomness of possible native structures instead of sequences. We observe that this situation emerges naturally if the distribution of conformational energies happens to arise from two independent contributions corresponding to sequence‐dependent and ‐independent terms. This construction is consistent with the hypothesis of a protein burial folding code, with native structures being determined by a modest amount of sequence‐dependent atomic burial information with sequence‐independent constraints imposed by unspecific hydrogen bond formation. More generally, an appropriate combination of sequence‐dependent and ‐independent information accommodates the possibility of an efficient structural encoding with the main physical requirement for folding, providing possible insight not only on the folding process but also on several aspects sequence evolution such as neutral networks, conformational coverage, and de novo gene emergence.

show abstract

“…This value, corresponding to an average of

2^{2.6} \approx 6

possible amino acids in each position, or from

\approx 5

\approx 8

considering the predominant range of

S_{0}

Section: Discussionsupporting

confidence: 89%

S^{seq} (T_{g})

than for

S^{seq} (T_{sel})

. In any case, regardless of any specific estimate, for each sequence that folds to a particular native structure, having energy close to

E_{normalN}

in that structure, there are many other sequences, corresponding to an entropy of

S^{seq} (T_{g}) - S^{seq} (T_{sel})

, that are unable to fold to that structure nor to any other structure.…”

Section: Theoretical Analysismentioning

confidence: 99%

Sequence‐dependent and ‐independent information in a combined random energy model for protein folding and coding

Pereira de Araújo

2023

Proteins

View full text Add to dashboard Cite

show abstract

“…In these margins, too many minimally frustrated regions might hinder functional evolution, while the presence of too many highly frustrated regions will prevent folding from happening 14 . Frustration conservation analysis within protein families can be used to define the theoretical limits to preserve function throughout evolution revealing the interplay between sequence, structure(s), dynamic and function 46 . Now that high-quality protein structure models can be obtained for the members of any protein family, our frustration conservation analysis strategy stands as a valuable tool to increase the level of functional annotation in biological databases 47 .…”

Section: Discussionmentioning

confidence: 99%

Local energetic frustration conservation in protein families and superfamilies

Freiberger,

Ruiz-Serra,

Pontes

et al. 2023

Nat Commun

Self Cite

View full text Add to dashboard Cite

Energetic local frustration offers a biophysical perspective to interpret the effects of sequence variability on protein families. Here we present a methodology to analyze local frustration patterns within protein families and superfamilies that allows us to uncover constraints related to stability and function, and identify differential frustration patterns in families with a common ancestry. We analyze these signals in very well studied protein families such as PDZ, SH3, ɑ and β globins and RAS families. Recent advances in protein structure prediction make it possible to analyze a vast majority of the protein space. An automatic and unsupervised proteome-wide analysis on the SARS-CoV-2 virus demonstrates the potential of our approach to enhance our understanding of the natural phenotypic diversity of protein families beyond single protein instances. We apply our method to modify biophysical properties of natural proteins based on their family properties, as well as perform unsupervised analysis of large datasets to shed light on the physicochemical signatures of poorly characterized proteins such as the ones belonging to emergent pathogens.

show abstract

“…In these margins, too many minimally frustrated regions might hinder functional evolution while the presence of too many highly frustrated regions will prevent folding from happening (39). Frustration conservation analysis within protein families can be used to define the theoretical limits to preserve function throughout evolution revealing the interplay between sequence, structure(s), dynamic and function(s) (40). Now that high quality protein structure models can be obtained for the members of any protein family, our frustration conservation analysis strategy stands as a valuable tool to increase the level of functional annotation in biological databases (41).…”

Section: Discussionmentioning

confidence: 99%

The Evolution of Local Energetic Frustration in Protein Families

Freiberger

Ruiz-Serra

Pontes

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Energetic local frustration offers a biophysical perspective to interpret the effects of sequence variability on protein families. Here we present a methodology to analyze local frustration patterns within protein families that allows us to uncover constraints related to stability and function, and identify differential frustration patterns in families with a common ancestry. We have analyzed these signals in very well studied cases such as PDZ, SH3, alpha and beta globins and RAS families. Recent advances in protein structure prediction make it possible to analyze a vast majority of the protein space. An automatic and unsupervised proteome-wide analysis on the SARS-CoV-2 virus demonstrates the potential of our approach to enhance our understanding of the natural phenotypic diversity of protein families beyond single protein instances. We have applied our method to modify biophysical properties of natural proteins based on their family properties, as well as perform unsupervised analysis of large datasets to shed light on the physicochemical signatures of poorly characterized proteins such as emergent pathogens.

show abstract

Molecular Information Theory Meets Protein Folding

Cited by 6 publications

References 117 publications

Sequence‐dependent and ‐independent information in a combined random energy model for protein folding and coding

Sequence‐dependent and ‐independent information in a combined random energy model for protein folding and coding

Local energetic frustration conservation in protein families and superfamilies

The Evolution of Local Energetic Frustration in Protein Families

Contact Info

Product

Resources

About