The key role of electrostatic interactions in the induced folding in RNA recognition by DCL1-A

Perspectives on the landscape and flux theory for describing emergent behaviors of the biological systems

We give a review on the landscape theory of the equilibrium biological systems and landscape-flux theory of the nonequilibrium biological systems as the global driving force. The emergences of the behaviors, the associated thermodynamics in terms of the entropy and free energy and dynamics in terms of the rate and paths have been quantitatively demonstrated. The hierarchical organization structures have been discussed. The biological applications ranging from protein folding, biomolecular recognition, specificity, biomolecular evolution and design for equilibrium systems as well as cell cycle, differentiation and development, cancer, neural networks and brain function, and evolution for nonequilibrium systems, cross-scale studies of genome structural dynamics and experimental quantifications/verifications of the landscape and flux are illustrated. Together, this gives an overall global physical and quantitative picture in terms of the landscape and flux for the behaviors, dynamics and functions of biological systems.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Protein electrostatics: From computational and structural analysis to discovery of functional fingerprints and biotechnological design

Computationally driven engineering of proteins aims to allow them to withstand an extended range of conditions and to mediate modified or novel functions. Therefore, it is crucial to the biotechnological industry, to biomedicine and to afford new challenges in environmental sciences, such as biocatalysis for green chemistry and bioremediation. In order to achieve these goals, it is important to clarify molecular mechanisms underlying proteins stability and modulating their interactions. So far, much attention has been given to hydrophobic and polar packing interactions and stability of the protein core. In contrast, the role of electrostatics and, in particular, of surface interactions has received less attention. However, electrostatics plays a pivotal role along the whole life cycle of a protein, since early folding steps to maturation, and it is involved in the regulation of protein localization and interactions with other cellular or artificial molecules. Short- and long-range electrostatic interactions, together with other forces, provide essential guidance cues in molecular and macromolecular assembly. We report here on methods for computing protein electrostatics and for individual or comparative analysis able to sort proteins by electrostatic similarity. Then, we provide examples of electrostatic analysis and fingerprints in natural protein evolution and in biotechnological design, in fields as diverse as biocatalysis, antibody and nanobody engineering, drug design and delivery, molecular virology, nanotechnology and regenerative medicine.

Non-specific DNA-driven quinary interactions promote structural transitions in proteins

The nature and distribution of charged residues on the surface of proteins play a vital role in determining the binding affinity, selectivity and kinetics of association to ligands. When it comes to DNA-binding domains (DBDs), these functional features manifest as anisotropic distribution of positively charged residues on the protein surface driven by the requirement to bind DNA, a highly negatively charged polymer. In this work, we compare the thermodynamic behavior of nine different proteins belonging to three families - LacR, engrailed and Brk - some of which are disordered in solution in the absence of DNA. Combining detailed electrostatic calculations and statistical mechanical modeling of folding landscapes at different distances and relative orientations with respect to DNA, we show that non-specific electrostatic interactions between the protein and DNA can promote structural transitions in DBDs. Such quinary interactions that are strictly agnostic to the DNA sequence induce varied behaviors including folding of disordered domains, partial unfolding of ordered proteins and (de-)population of intermediate states. Our work highlights that the folding landscape of proteins can be tuned as a function of distance from DNA and hints at possible reasons for DBDs exhibiting complex kinetic-thermodynamic behaviors in the absence of DNA.

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions

The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.

Probing the Basis of α-Synuclein Aggregation by Comparing Simulations to Single-Molecule Experiments

Intrinsically disordered proteins often play an important role in protein aggregation. However, it is challenging to determine the structures and interactions that drive the early stages of aggregation because they are transient and obscured in a heterogeneous mixture of disordered states. Even computational methods are limited because the lack of ordered structure makes it difficult to ensure that the relevant conformations are sampled. We address these challenges by integrating atomistic simulations with high-resolution single-molecule measurements reported previously, using the measurements to help discern which parts of the disordered ensemble of structures in the simulations are most probable while using the simulations to identify residues and interactions that are important for oligomer stability. This approach was applied to α-synuclein, an intrinsically disordered protein that aggregates in the context of Parkinson's disease. We simulated single-molecule pulling experiments on dimers, the minimal oligomer, and compared them to force spectroscopy measurements. Force-extension curves were simulated starting from a set of 66 structures with substantial structured content selected from the ensemble of dimer structures generated at zero force via Monte Carlo simulations. The pattern of contour length changes as the structures unfolded through intermediate states was compared to the results from optical trapping measurements on the same dimer to discern likely structures occurring in the measurements. Simulated pulling curves were generally consistent with experimental data but with a larger number of transient intermediates. We identified an ensemble of β-rich dimer structures consistent with the experimental data from which dimer interfaces could be deduced. These results suggest specific druggable targets in the structural motifs of α-synuclein that may help prevent the earliest steps of oligomerization.

Tunable order-disorder continuum in protein-DNA interactions

DNA-binding protein domains (DBDs) sample diverse conformations in equilibrium facilitating the search and recognition of specific sites on DNA over millions of energetically degenerate competing sites. We hypothesize that DBDs have co-evolved to sense and exploit the strong electric potential from the array of negatively charged phosphate groups on DNA. We test our hypothesis by employing the intrinsically disordered DBD of cytidine repressor (CytR) as a model system. CytR displays a graded increase in structure, stability and folding rate on increasing the osmolarity of the solution that mimics the non-specific screening by DNA phosphates. Electrostatic calculations and an Ising-like statistical mechanical model predict that CytR exhibits features of an electric potential sensor modulating its dimensions and landscape in a unique distance-dependent manner, while DNA plays the role of a non-specific macromolecular chaperone. Accordingly, CytR binds its natural half-site faster than the diffusion-controlled limit and even random DNA conforming to an electrostatic-steering binding mechanism. Our work unravels for the first time the synergistic features of a natural electrostatic potential sensor, a novel binding mechanism driven by electrostatic frustration and disorder, and the role of DNA in promoting distance-dependent protein structural transitions critical for switching between specific and non-specific DNA-binding modes.

The Effect of Electrostatic Interactions on the Folding Kinetics of a 3-α-Helical Bundle Protein Family

The trio of protein segment repeats called spectrins diverges by more than 2 orders of magnitude in their folding and unfolding rates, despite having very similar stabilities and almost coincidental topologies. Experimental studies revealed that the mutation of five particular residues dramatically alters the kinetic rates in the slow folders, making them similar to the rates of the fast folder. This is considered to be an exceptional behavior which seems in principle to challenge the current understanding of the protein folding process. In this work, we analyze this scenario, using a simplified computational model, combined with state-of-the-art kinetic analysis techniques. Our model faithfully separates the kinetics of the fast and slow folders and captures the effect of the five mutations. We show that the inclusion of electrostatics in the model is necessary to explain the experimental findings.