Distinction and Quantification of Noncovalent Dispersive and Hydrophobic Effects

The possibilities of comparing computational results of noncovalent interactions with experimental data are discussed, first with respect to intramolecular interactions. For these a variety of experimental data such as heats of formation, crystal sublimation heats, comparison with energy minimized structures, and spectroscopic data are available, but until now largely have not found widespread application. Early force field and QM/MP2 calculations have already shown that the sublimation heats of hydrocarbons can be predicted with an accuracy of ±1%. Intermolecular interactions in solution or the gas phase are always accompanied by difficult to compute entropic contributions, like all associations between molecules. Experimentally observed T∆S values contribute 10% to 80% of the total ∆G, depending on interaction mechanisms within the complexes, such as, e.g., hydrogen bonding and ion pairing. Free energies ∆G derived from equilibrium measurements in solution allow us to define binding increments ∆∆G, which are additive and transferable to a variety of supramolecular complexes. Data from more than 90 equilibrium measurements of porphyrin receptors in water indicate that small alkanes do not bind to the hydrophobic flat surfaces within a measuring limit of ∆G = ±0.5 kJ/mol, and that 20 functions bearing heteroatoms show associations by dispersive interactions with up to ∆G = 8 kJ/mol, roughly as a function of their polarizability. Aromatic systems display size-dependent affinities ∆G as a linear function of the number of π-electrons.

Fuzzy Drug Targets: Disordered Proteins in the Drug-Discovery Realm

Intrinsically disordered proteins (IDPs) and regions (IDRs) form a large part of the eukaryotic proteome. Contrary to the structure-function paradigm, the disordered proteins perform a myriad of functions in vivo. Consequently, they are involved in various disease pathways and are plausible drug targets. Unlike folded proteins, that have a defined structure and well carved out drug-binding pockets that can guide lead molecule selection, the disordered proteins require alternative drug-development methodologies that are based on an acceptable picture of their conformational ensemble. In this review, we discuss various experimental and computational techniques that contribute toward understanding IDP "structure" and describe representative pursuances toward IDP-targeting drug development. We also discuss ideas on developing rational drug design protocols targeting IDPs.

The longitudinal neural dynamics changes of whole brain connectome during natural recovery from poststroke aphasia

Poststroke aphasia is one of the most dramatic functional deficits that results from direct damage of focal brain regions and dysfunction of large-scale brain networks. The reconstruction of language function depends on the hierarchical whole-brain dynamic reorganization. However, investigations into the longitudinal neural changes of large-scale brain networks for poststroke aphasia remain scarce. Here we characterize large-scale brain dynamics in left-frontal-stroke aphasia through energy landscape analysis. Using fMRI during an auditory comprehension task, we find that aphasia patients suffer serious whole-brain dynamics perturbation in the acute and subacute stages after stroke, in which the brains were restricted into two major activity patterns. Following spontaneous recovery process, the brain flexibility improved in the chronic stage. Critically, we demonstrated that the abnormal neural dynamics are correlated with the aberrant brain network coordination. Taken together, the energy landscape analysis exhibited that the acute poststroke aphasia has a constrained, low dimensional brain dynamics, which were replaced by less constrained and high dimensional dynamics at chronic aphasia. Our study provides a new perspective to profoundly understand the pathological mechanisms of poststroke aphasia.

Conformational Dynamics in Extended RGD-Containing Peptides

RGD is a prolific example of a tripeptide used in biomaterials for cell adhesion, but the potency of free or surface-bound RGD tripeptide is orders-of-magnitude less than the RGD domain within natural proteins. We designed a set of peptides with varying lengths, composed of fragments of fibronectin protein whose central three residues are RGD, in order to vary their conformational behavior without changing the binding site's chemical environment. With these peptides, we measure the conformational dynamics and transient structure of the active site. Our studies reveal how flanking residues affect conformational behavior and integrin binding. We find that disorder of the binding site is important to the potency of RGD peptides and that transient hydrogen bonding near the RGD site affects both the energy landscape roughness of the peptides and peptide binding. This phenomenon is independent of longer-range folding interactions and helps explain why short binding sequences, including RGD itself, do not fully replicate the integrin-targeting properties of extracellular matrix proteins. Our studies reinforce that peptide binding is a holistic event and fragments larger than those directly involved in binding should be considered in the design of peptide epitopes for functional biomaterials.

Quantifying residue-specific conformational dynamics of a highly reactive 29-mer peptide

Understanding structural transitions within macromolecules remains an important challenge in biochemistry, with important implications for drug development and medicine. Insight into molecular behavior often requires residue-specific dynamics measurement at micromolar concentrations. We studied MP01-Gen4, a library peptide selected to rapidly undergo bioconjugation, by using electron paramagnetic resonance (EPR) to measure conformational dynamics. We mapped the dynamics of MP01-Gen4 with residue-specificity and identified the regions involved in a structural transformation related to the conjugation reaction. Upon reaction, the conformational dynamics of residues near the termini slow significantly more than central residues, indicating that the reaction induces a structural transition far from the reaction site. Arrhenius analysis demonstrates a nearly threefold decrease in the activation energy of conformational diffusion upon reaction (8.0 kBT to 3.4 kBT), which occurs across the entire peptide, independently of residue position. This novel approach to EPR spectral analysis provides insight into the positional extent of disorder and the nature of the energy landscape of a highly reactive, intrinsically disordered library peptide before and after conjugation.

Unravelling the complexity of signalling networks in cancer: A review of the increasing role for computational modelling

Cancer induction is a highly complex process involving hundreds of different inducers but whose eventual outcome is the same. Clearly, it is essential to understand how signalling pathways and networks generated by these inducers interact to regulate cell behaviour and create the cancer phenotype. While enormous strides have been made in identifying key networking profiles, the amount of data generated far exceeds our ability to understand how it all "fits together". The number of potential interactions is astronomically large and requires novel approaches and extreme computation methods to dissect them out. However, such methodologies have high intrinsic mathematical and conceptual content which is difficult to follow. This review explains how computation modelling is progressively finding solutions and also revealing unexpected and unpredictable nano-scale molecular behaviours extremely relevant to how signalling and networking are coherently integrated. It is divided into linked sections illustrated by numerous figures from the literature describing different approaches and offering visual portrayals of networking and major conceptual advances in the field. First, the problem of signalling complexity and data collection is illustrated for only a small selection of known oncogenes. Next, new concepts from biophysics, molecular behaviours, kinetics, organisation at the nano level and predictive models are presented. These areas include: visual representations of networking, Energy Landscapes and energy transfer/dissemination (entropy); diffusion, percolation; molecular crowding; protein allostery; quinary structure and fractal distributions; energy management, metabolism and re-examination of the Warburg effect. The importance of unravelling complex network interactions is then illustrated for some widely-used drugs in cancer therapy whose interactions are very extensive. Finally, use of computational modelling to develop micro- and nano- functional models ("bottom-up" research) is highlighted. The review concludes that computational modelling is an essential part of cancer research and is vital to understanding network formation and molecular behaviours that are associated with it. Its role is increasingly essential because it is unravelling the huge complexity of cancer induction otherwise unattainable by any other approach.

The roughness of the protein energy landscape results in anomalous diffusion of the polypeptide backbone

Recombination of photolysed protein disulfide bonds confirms subdiffusional backbone motion and measures the roughness of the protein's energy landscape.

Physicochemical bases for protein folding, dynamics, and protein-ligand binding

Proteins are essential parts of living organisms and participate in virtually every process within cells. As the genomic sequences for increasing number of organisms are completed, research into how proteins can perform such a variety of functions has become much more intensive because the value of the genomic sequences relies on the accuracy of understanding the encoded gene products. Although the static three-dimensional structures of many proteins are known, the functions of proteins are ultimately governed by their dynamic characteristics, including the folding process, conformational fluctuations, molecular motions, and protein-ligand interactions. In this review, the physicochemical principles underlying these dynamic processes are discussed in depth based on the free energy landscape (FEL) theory. Questions of why and how proteins fold into their native conformational states, why proteins are inherently dynamic, and how their dynamic personalities govern protein functions are answered. This paper will contribute to the understanding of structure-function relationship of proteins in the post-genome era of life science research.

Stabilization of a protein conferred by an increase in folded state entropy

Entropic stabilization of native protein structures typically relies on strategies that serve to decrease the entropy of the unfolded state. Here we report, using a combination of experimental and computational approaches, on enhanced thermodynamic stability conferred by an increase in the configurational entropy of the folded state. The enhanced stability is observed upon modifications of a loop region in the enzyme acylphosphatase and is achieved despite significant enthalpy losses. The modifications that lead to increased stability, as well as those that result in destabilization, however, strongly compromise enzymatic activity, rationalizing the preservation of the native loop structure even though it does not provide the protein with maximal stability or kinetic foldability.

Connecting the kinetics and energy landscape of tRNA translocation on the ribosome

Functional rearrangements in biomolecular assemblies result from diffusion across an underlying energy landscape. While bulk kinetic measurements rely on discrete state-like approximations to the energy landscape, single-molecule methods can project the free energy onto specific coordinates. With measures of the diffusion, one may establish a quantitative bridge between state-like kinetic measurements and the continuous energy landscape. We used an all-atom molecular dynamics simulation of the 70S ribosome (2.1 million atoms; 1.3 microseconds) to provide this bridge for specific conformational events associated with the process of tRNA translocation. Starting from a pre-translocation configuration, we identified sets of residues that collectively undergo rotary rearrangements implicated in ribosome function. Estimates of the diffusion coefficients along these collective coordinates for translocation were then used to interconvert between experimental rates and measures of the energy landscape. This analysis, in conjunction with previously reported experimental rates of translocation, provides an upper-bound estimate of the free-energy barriers associated with translocation. While this analysis was performed for a particular kinetic scheme of translocation, the quantitative framework is general and may be applied to energetic and kinetic descriptions that include any number of intermediates and transition states.

Intrinsically disordered proteins in a physics-based world

Intrinsically disordered proteins (IDPs) are a newly recognized class of functional proteins that rely on a lack of stable structure for function. They are highly prevalent in biology, play fundamental roles, and are extensively involved in human diseases. For signaling and regulation, IDPs often fold into stable structures upon binding to specific targets. The mechanisms of these coupled binding and folding processes are of significant importance because they underlie the organization of regulatory networks that dictate various aspects of cellular decision-making. This review first discusses the challenge in detailed experimental characterization of these heterogeneous and dynamics proteins and the unique and exciting opportunity for physics-based modeling to make crucial contributions, and then summarizes key lessons from recent de novo simulations of the structure and interactions of several regulatory IDPs.

The implications of gene heterozygosity for protein folding and protein turnover

The offspring of closely related parents often suffer from inbreeding depression, sometimes resulting in a slower growth rate for inbred offspring relative to non-inbred offspring. Previous research has shown that some of the slower growth rate of inbred organisms can be attributed to the inbred organisms' increased levels of protein turnover. This paper attempts to show that the higher levels of protein turnover among inbred organisms can be attributed to accumulations of misfolded and aggregated proteins that require degradation by the inbred organisms' protein quality control systems. The accumulation of misfolded and aggregated proteins within inbred organisms are the result of more negative free energies of folding for proteins encoded at homozygous gene loci and higher concentrations of potentially aggregating non-native protein species within the cell. The theory presented here makes several quantitative predictions that suggest a connection between protein misfolding/aggregation and polyploidy that can be tested by future research.