Prediction of protein relative enthalpic stability from molecular dynamics simulations of the folded and unfolded states

Protein Thermodynamic Properties, Crystallisation, and the Hofmeister Series

The Hofmeister series is a series of ions ordered according to their ability to precipitate proteins. It has also been linked to a host of (bio)chemical phenomena. Several attempts over the years to correlate the series to the varying success of different salts in crystallising proteins have been largely inconclusive. A correlation, based on published data and crystallisation conditions for several proteins, is proposed here between some thermodynamic properties of proteins and the position in the Hofmeister series of the salts from which they preferentially crystallise. Namely, a high ratio between the entropic or enthalpic protein-solvent interactions contribution to thermodynamic stability and the total thermodynamic stability of a given protein, indicate the protein's high propensity to crystallise in solutions of highly kosmotropic salts. Low such ratios on the other hand, indicate that chaotropic salts can be equally successful, i. e. that the protein in question is rather indifferent to the Hofmeister character of the salt. Testing various model proteins for crystallisation against screens containing salts found at different points on the Hofmeister series, as well as further bibliographic analysis, have yielded results that appear to largely corroborate this hypothesis. These conclusions may conceivably be used as a crystallisation predictive tool.

Biomolecular dynamics in the 21st century

The relevance of motions in biological macromolecules has been clear since the early structural analyses of proteins by X-ray crystallography. Computer simulations have been applied to provide a deeper understanding of the dynamics of biological macromolecules since 1976, and are now a standard tool in many labs working on the structure and function of biomolecules. In this mini-review we highlight some areas of current interest and active development for simulations, in particular all-atom molecular dynamics simulations.

MEnTaT: A machine-learning approach for the identification of mutations to increase protein stability

Enhancing protein thermal stability is important for biomedical and industrial applications as well as in the research laboratory. Here, we describe a simple machine-learning method which identifies amino acid substitutions that contribute to thermal stability based on comparison of the amino acid sequences of homologous proteins derived from bacteria that grow at different temperatures. A key feature of the method is that it compares the sequences based not simply on the amino acid identity, but rather on the structural and physicochemical properties of the side chain. The method accurately identified stabilizing substitutions in three well-studied systems and was validated prospectively by experimentally testing predicted stabilizing substitutions in a polyamine oxidase. In each case, the method outperformed the widely used bioinformatic consensus approach. The method can also provide insight into fundamental aspects of protein structure, for example, by identifying how many sequence positions in a given protein are relevant to temperature adaptation.

Role of simple descriptors and applicability domain in predicting change in protein thermostability

The melting temperature (Tm) of a protein is the temperature at which half of the protein population is in a folded state. Therefore, Tm is a measure of the thermostability of a protein. Increasing the Tm of a protein is a critical goal in biotechnology and biomedicine. However, predicting the change in melting temperature (dTm) due to mutations at a single residue is difficult because it depends on an intricate balance of forces. Existing methods for predicting dTm have had similar levels of success using generally complex models. We find that training a machine learning model with a simple set of easy to calculate physicochemical descriptors describing the local environment of the mutation performed as well as more complicated machine learning models and is 2-6 orders of magnitude faster. Importantly, unlike in most previous publications, we perform a blind prospective test on our simple model by designing 96 variants of a protein not in the training set. Results from retrospective and prospective predictions reveal the limited applicability domain of each model. This study highlights the current deficiencies in the available dTm dataset and is a call to the community to systematically design a larger and more diverse experimental dataset of mutants to prospectively predict dTm with greater certainty.

Dynamical Network of HIV-1 Protease Mutants Reveals the Mechanism of Drug Resistance and Unhindered Activity

HIV-1 protease variants resist drugs by active and non-active-site mutations. The active-site mutations, which are the primary or first set of mutations, hamper the stability of the enzyme and resist the drugs minimally. As a result, secondary mutations that not only increase protein stability for unhindered catalytic activity but also resist drugs very effectively arise. While the mechanism of drug resistance of the active-site mutations is through modulating the active-site pocket volume, the mechanism of drug resistance of the non-active-site mutations is unclear. Moreover, how these allosteric mutations, which are 8-21 Å distant, communicate to the active site for drug efflux is completely unexplored. Results from molecular dynamics simulations suggest that the primary mechanism of drug resistance of the secondary mutations involves opening of the flexible protease flaps. Results from both residue- and community-based network analyses reveal that this precise action of protease is accomplished by the presence of robust communication paths between the mutational sites and the functionally relevant regions: active site and flaps. While the communication is more direct in the wild type, it traverses across multiple intermediate residues in mutants, leading to weak signaling and unregulated motions of flaps. The global integrity of the protease network is, however, maintained through the neighboring residues, which exhibit high degrees of conservation, consistent with clinical data and mutagenesis studies.