Coevolutionary Signals and Structure-Based Models for the Prediction of Protein Native Conformations

Adaptive evolution and co-evolution of chloroplast genomes in Pteridaceae species occupying different habitats: overlapping residues are always highly mutated

BACKGROUND

The evolution of protein residues depends on the mutation rates of their encoding nucleotides, but it may also be affected by co-evolution with other residues. Chloroplasts function as environmental sensors, transforming fluctuating environmental signals into different physiological responses. We reasoned that habitat diversity may affect their rate and mode of evolution, which might be evidenced in the chloroplast genome. The Pteridaceae family of ferns occupy an unusually broad range of ecological niches, which provides an ideal system for analysis.

RESULTS

We conducted adaptive evolution and intra-molecular co-evolution analyses of Pteridaceae chloroplast DNAs (cpDNAs). The results indicate that the residues undergoing adaptive evolution and co-evolution were mostly independent, with only a few residues being simultaneously involved in both processes, and these overlapping residues tend to exhibit high mutations. Additionally, our data showed that Pteridaceae chloroplast genes are under purifying selection. Regardless of whether we grouped species by lineage (which corresponded with ecological niches), we determined that positively selected residues mainly target photosynthetic genes.

CONCLUSIONS

Our work provides evidence for the adaptive evolution of Pteridaceae cpDNAs, especially photosynthetic genes, to different habitats and sheds light on the adaptive evolution and co-evolution of proteins.

Metamorphic proteins under a computational microscope: Lessons from a fold-switching RfaH protein

Metamorphic proteins constitute unexpected paradigms of the protein folding problem, as their sequences encode two alternative folds, which reversibly interconvert within biologically relevant timescales to trigger different cellular responses. Once considered a rare aberration, metamorphism may be common among proteins that must respond to rapidly changing environments, exemplified by NusG-like proteins, the only transcription factors present in every domain of life. RfaH, a specialized paralog of bacterial NusG, undergoes an all-α to all-β domain switch to activate expression of virulence and conjugation genes in many animal and plant pathogens and is the quintessential example of a metamorphic protein. The dramatic nature of RfaH structural transformation and the richness of its evolutionary history makes for an excellent model for studying how metamorphic proteins switch folds. Here, we summarize the structural and functional evidence that sparked the discovery of RfaH as a metamorphic protein, the experimental and computational approaches that enabled the description of the molecular mechanism and refolding pathways of its structural interconversion, and the ongoing efforts to find signatures and general properties to ultimately describe the protein metamorphome.

Computational compensatory mutation discovery approach: Predicting a PARP1 variant rescue mutation

The prediction of protein mutations that affect function may be exploited for multiple uses. In the context of disease variants, the prediction of compensatory mutations that reestablish functional phenotypes could aid in the development of genetic therapies. In this work, we present an integrated approach that combines coevolutionary analysis and molecular dynamics (MD) simulations to discover functional compensatory mutations. This approach is employed to investigate possible rescue mutations of a poly(ADP-ribose) polymerase 1 (PARP1) variant, PARP1 V762A, associated with lung cancer and follicular lymphoma. MD simulations show PARP1 V762A exhibits noticeable changes in structural and dynamical behavior compared with wild-type (WT) PARP1. Our integrated approach predicts A755E as a possible compensatory mutation based on coevolutionary information, and molecular simulations indicate that the PARP1 A755E/V762A double mutant exhibits similar structural and dynamical behavior to WT PARP1. Our methodology can be broadly applied to a large number of systems where single-nucleotide polymorphisms have been identified as connected to disease and can shed light on the biophysical effects of such changes as well as provide a way to discover potential mutants that could restore WT-like functionality. This can, in turn, be further utilized in the design of molecular therapeutics that aim to mimic such compensatory effect.

Coevolution-derived native and non-native contacts determine the emergence of a novel fold in a universally conserved family of transcription factors

The NusG protein family is structurally and functionally conserved in all domains of life. Its members directly bind RNA polymerases and regulate transcription processivity and termination. RfaH, a divergent sub-family in its evolutionary history, is known for displaying distinct features than those in NusG proteins, which allows them to regulate the expression of virulence factors in enterobacteria in a DNA sequence-dependent manner. A striking feature is its structural interconversion between an active fold, which is the canonical NusG three-dimensional structure, and an autoinhibited fold, which is distinctively novel. How this novel fold is encoded within RfaH sequence to encode a metamorphic protein remains elusive. In this work, we used publicly available genomic RfaH protein sequences to construct a complete multiple sequence alignment, which was further augmented with metagenomic sequences and curated by predicting their secondary structure propensities using JPred. Coevolving pairs of residues were calculated from these sequences using plmDCA and GREMLIN, which allowed us to detect the enrichment of key metamorphic contacts after sequence filtering. Finally, we combined our coevolutionary predictions with molecular dynamics to demonstrate that these interactions are sufficient to predict the structures of both native folds, where coevolutionary-derived non-native contacts may play a key role in achieving the compact RfaH novel fold. All in all, emergent coevolutionary signals found within RfaH sequences encode the autoinhibited and active folds of this protein, shedding light on the key interactions responsible for the action of this metamorphic protein.

Structural discrimination analysis for constraint selection in protein modeling

MOTIVATION

Protein structure modeling can be improved by the use of distance constraints between amino acid residues, provided such data reflects-at least partially-the native tertiary structure of the target system. In fact, only a small subset of the native contact map is necessary to successfully drive the model conformational search, so one important goal is to obtain the set of constraints with the highest true-positive rate, lowest redundancy, and greatest amount of information. In this work, we introduce a constraint evaluation and selection method based on the point-biserial correlation coefficient, which utilizes structural information from an ensemble of models to indirectly measure the power of each constraint in biasing the conformational search towards consensus structures.

RESULTS

Residue contact maps obtained by direct coupling analysis are systematically improved by means of discriminant analysis, reaching in some cases accuracies often seen only in modern deep-learning based approaches. When combined with an iterative modeling workflow, the proposed constraint classification optimizes the selection of the constraint set and maximizes the probability of obtaining successful models. The use of discriminant analysis for the valorization of the information of constraint data sets is a general concept with possible applications to other constraint types and modeling problems.

AVAILABILITY AND IMPLEMENTATION

scripts and procedures to implement the methodology presented herein are available at https://github.com/m3g/2021_Bottino_Biserial.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Substitution of a Surface-Exposed Residue Involved in an Allosteric Network Enhances Tryptophan Synthase Function in Cells

Networks of noncovalent amino acid interactions propagate allosteric signals throughout proteins. Tryptophan synthase (TS) is an allosterically controlled bienzyme in which the indole product of the alpha subunit (αTS) is transferred through a 25 Å hydrophobic tunnel to the active site of the beta subunit (βTS). Previous nuclear magnetic resonance and molecular dynamics simulations identified allosteric networks in αTS important for its function. We show here that substitution of a distant, surface-exposed network residue in αTS enhances tryptophan production, not by activating αTS function, but through dynamically controlling the opening of the indole channel and stimulating βTS activity. While stimulation is modest, the substitution also enhances cell growth in a tryptophan-auxotrophic strain of Escherichia coli compared to complementation with wild-type αTS, emphasizing the biological importance of the network. Surface-exposed networks provide new opportunities in allosteric drug design and protein engineering, and hint at potential information conduits through which the functions of a metabolon or even larger proteome might be coordinated and regulated.

Driving Protein Conformational Cycles in Physiology and Disease: "Frustrated" Amino Acid Interaction Networks Define Dynamic Energy Landscapes: Amino Acid Interaction Networks Change Progressively Along Alpha Tryptophan Synthase's Catalytic Cycle

A general framework by which dynamic interactions within a protein will promote the necessary series of structural changes, or "conformational cycle," required for function is proposed. It is suggested that the free-energy landscape of a protein is biased toward this conformational cycle. Fluctuations into higher energy, although thermally accessible, conformations drive the conformational cycle forward. The amino acid interaction network is defined as those intraprotein interactions that contribute most to the free-energy landscape. Some network connections are consistent in every structural state, while others periodically change their interaction strength according to the conformational cycle. It is reviewed here that structural transitions change these periodic network connections, which then predisposes the protein toward the next set of network changes, and hence the next structural change. These concepts are illustrated by recent work on tryptophan synthase. Disruption of these dynamic connections may lead to aberrant protein function and disease states.