Modeling Alternate Conformations with Alphafold2 via Modification of the Multiple Sequence Alignment

Mchaourab, Hassane S.

doi:10.1101/2021.11.29.470469

Cited by 17 publications

(21 citation statements)

References 37 publications

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“…Their results also further demonstrate the accuracy of AlphaFold2 in predicting protein structures. Coin-cidentally, Alamo et al recently predicted different conformations of some transporters through AlphaFold2, which provided a basis for our research [38]. Differently, in our study, we focused on the interference of MSA information by mutating multiple residues, thus affecting coevolving residue pairs.…”

Section: Discussionmentioning

confidence: 91%

Utilization of AlphaFold2 to Predict MFS Protein Conformations after Selective Mutation

Xiao

Wang

et al. 2022

IJMS

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The major facilitator superfamily (MFS) is the largest secondary transporter family and is responsible for transporting a broad range of substrates across the biomembrane. These proteins are involved in a series of conformational changes during substrate transport. To decipher the transport mechanism, it is necessary to obtain structures of these different conformations. At present, great progress has been made in predicting protein structure based on coevolutionary information. In this study, AlphaFold2 was used to predict different conformational structures for 69 MFS transporters of E. coli after the selective mutation of residues at the interface between the N- and C-terminal domains. The predicted structures for these mutants had small RMSD values when compared to structures obtained using X-ray crystallography, which indicates that AlphaFold2 predicts the structure of MSF transporters with high accuracy. In addition, different conformations of other transporter family proteins have been successfully predicted based on mutation methods. This study provides a structural basis to study the transporting mechanism of the MFS transporters and a method to probe dynamic conformation changes of transporter family proteins when performing their function.

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Section: Discussionmentioning

confidence: 91%

Utilization of AlphaFold2 to Predict MFS Protein Conformations after Selective Mutation

Xiao

Wang

et al. 2022

IJMS

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“…If AlphaFold2 can recover the inherent conformational heterogeneity in the bound/modified forms of IDRs/IDPs, such predictions would be valuable to generate testable hypotheses and search for plausible structured conformations that are accessible to a given IDR/IDP. Interestingly, two recent studies proposed that either reducing the depth of the MSA or performing rational in silico mutagenesis within the MSA can drive AlphaFold2 to sample alternate conformations (Alamo et al 2021; Stein & Mchaourab 2021). It remains to be tested if this approach is generally applicable and amenable to IDRs/IDPs.…”

Section: Discussionmentioning

confidence: 99%

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

Alderson

Pritišanac

Moses

et al. 2022

Preprint

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Deep learning-based approaches to protein structure prediction, such as AlphaFold2 and RoseTTAFold, can now define many protein structures with atomic-level accuracy. The AlphaFold Protein Structure Database (AFDB) contains a predicted structure for nearly every protein in the human proteome, including proteins that have intrinsically disordered regions (IDRs), which do not adopt a stable structure and rapidly interconvert between conformations. Although it is generally assumed that IDRs have very low AlphaFold2 confidence scores that reflect low-confidence structural predictions, we show here that AlphaFold2 assigns confident structures to nearly 15% of human IDRs. The amino-acid sequences of IDRs with high-confidence structures do not show significant similarity to the Protein Data Bank; instead, these IDR sequences exhibit a higher degree of positional amino-acid sequence conservation and are more enriched in charged and hydrophobic residues than IDRs with low-confidence structures. We compared the AlphaFold2 predictions to experimental NMR data for a subset of IDRs known to fold under specific conditions, finding that AlphaFold2 tends to capture the folded state structure. We note, however, that these AlphaFold2 predictions cannot detect functionally relevant structural plasticity within IDRs and cannot offer an ensemble representation of IDRs. Nevertheless, AlphaFold2 assigns high-confidence scores to about 60% of a set of 350 IDRs that have been reported to conditionally fold, suggesting that AlphaFold2 has learned to identify conditionally folded IDRs, which is unexpected, since IDRs were minimally represented in the training data. Leveraging this ability to discover IDRs that conditionally fold, we find that up to 80% of IDRs in archaea and bacteria are predicted to conditionally fold, but less than 20% of eukaryotic IDRs. Our results suggest that a large majority of IDRs in the proteomes of human and other eukaryotes would be expected to function in the absence of conditional folding.

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“…In this case, and when confronted to conflictual modeling, alternate instantiations or differences in arbitrations by the AF2A self-attention algorithms resulted in prediction of markedly different conformations from run to run. As recently reported, the reference sequence sets used or the choice of alternate cluster centroid definitions during the multiple sequence alignment step might also play a role in the generation of alternate solution 27 . Considering that the algorithm does not explicitly involve energetic calculation but is mostly based on geometric pattern recognition, such arbitrations are not expected to directly mirror relative thermodynamic stability of predicted complexes.…”

Section: Resultsmentioning

confidence: 99%

Confrontation of AlphaFold2 models with cryo-EM and crystal structures enlightens alternate geometries of the CYP102A1 multidomain protein

Urban

Pompon

2022

Preprint

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Large range structural dynamics plays a critical role for the function of electron transfer proteins. This information is generally not available from crystallographic structures, while cryo-electron microscopy (cryo-EM) can provide some elements but frequently with a degraded spatial resolution. Recently, AlphaFold-based structural modelling was extended to the prediction of protein complexes. In this work, bacterial CYP102A1 from Priestia megaterium was used as a test case to evaluate the capability of AlphaFold2 to predict alternative structures critical for catalysis. CYP102A1 monooxygenase, a NADPH-supported fatty acid hydroxylase, works as a soluble homodimer, each monomer harboring two flavins (FAD and FMN) and one heme cofactors. Large conformational changes are required during catalytic cycle to allow successive electron transfers from FAD to FMN and finally heme iron. We used the recently released AlphaFold2_advanced notebook (AF2A), to predict the possible alternate conformations supporting electron transfers in CYP102A1 homodimer. Challenging AF2A-derived models with previously reported experimental data revealed an unforeseen domain connectivity of the diflavin reductase part of the enzyme. Intermolecular crossed complex constitutes a novel type of structural organization never previously described. The predicted formation within the dimer of a stable complex between the heme containing domains was challenged and found consistent with uninterpreted features of reported crystallographic structures and cryo-EM imaging. The particularly efficient CYP102A1 catalytic mechanism was revisited to the light of the new evidenced connectivity in which the FMN-binding domain of each monomer oscillates on themselves to alternatively receive and transfer electrons without needing large structural change in the dimer. Such model was found explanatory for previously contradictory reported biochemical data. Possibility to mimic CYP102A1 structural organization into bicomponent eukaryotic P450 systems was evaluated by designing and modeling in silico synthetic reductase domains built from composite sequence segments from P. megaterium and human origins. More generally, this work illustrates how the ability of AF2A to predict alternate complex structures can enlighten and explain conformational changes critical for bio-assemblies.

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Modeling Alternate Conformations with Alphafold2 via Modification of the Multiple Sequence Alignment

Cited by 17 publications

References 37 publications

Utilization of AlphaFold2 to Predict MFS Protein Conformations after Selective Mutation

Utilization of AlphaFold2 to Predict MFS Protein Conformations after Selective Mutation

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

Confrontation of AlphaFold2 models with cryo-EM and crystal structures enlightens alternate geometries of the CYP102A1 multidomain protein

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