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Protein kinases are key actors of signaling networks and important drug targets. They cycle between active and inactive conformations, distinguished by a few elements within the catalytic domain. One is the activation loop, whose conserved DFG motif can occupy DFG‐in, DFG‐out, and some rarer conformations. Annotation and classification of the structural kinome are important, as different conformations can be targeted by different inhibitors and activators. Valuable resources exist; however, large‐scale applications will benefit from increased automation and interpretability of structural annotation. Interpretable machine learning models are described for this purpose, based on ensembles of decision trees. To train them, a set of catalytic domain sequences and structures was collected, somewhat larger and more diverse than existing resources. The structures were clustered based on the DFG conformation and manually annotated. They were then used as training input. Two main models were constructed, which distinguished active/inactive and in/out/other DFG conformations. They considered initially 1692 structural variables, spanning the whole catalytic domain, then identified ("learned") a small subset that sufficed for accurate classification. The first model correctly labeled all but 3 of 3289 structures as active or inactive, while the second assigned the correct DFG label to all but 17 of 8826 structures. The most potent classifying variables were all related to well‐known structural elements in or near the activation loop and their ranking gives insights into the conformational preferences. The models were used to automatically annotate 3850 kinase structures predicted recently with the Alphafold2 tool, showing that Alphafold2 reproduced the active/inactive but not the DFG‐in proportions seen in the Protein Data Bank. We expect the models will be useful for understanding and engineering kinases. [ABSTRACT FROM AUTHOR]

Computational protein design relies on simulations of a protein structure, where selected amino acids can mutate randomly, and mutations are selected to enhance a target property, such as stability. Often, the protein backbone is held fixed and its degrees of freedom are modeled implicitly to reduce the complexity of the conformational space. We present a hybrid method where short molecular dynamics (MD) segments are used to explore conformations and alternate with Monte Carlo (MC) moves that apply mutations to side chains. The backbone is fully flexible during MD. As a test, we computed side chain acid/base constants or pKa's in five proteins. This problem can be considered a special case of protein design, with protonation/deprotonation playing the role of mutations. The solvent was modeled as a dielectric continuum. Due to cost, in each protein we allowed just one side chain position to change its protonation state and the other position to change its type or mutate. The pKa's were computed with a standard method that scans a range of pH values and with a new method that uses adaptive landscape flattening (ALF) to sample all protonation states in a single simulation. The hybrid method gave notably better accuracy than standard, fixed-backbone MC. ALF decreased the computational cost a factor of 13. [ABSTRACT FROM AUTHOR]

Enzyme design is an important application of computational protein design (CPD). It can benefit enormously from the additional chemistries provided by noncanonical amino acids (ncAAs). These can be incorporated into an 'expanded' genetic code, and introduced in vivo into target proteins. The key step for genetic code expansion is to engineer an aminoacyl-transfer RNA (tRNA) synthetase (aaRS) and an associated tRNA that handles the ncAA. Experimental directed evolution has been successfully used to engineer aaRSs and incorporate over 200 ncAAs into expanded codes. But directed evolution has severe limits, and is not yet applicable to noncanonical AA backbones. CPD can help address several of its limitations, and has begun to be applied to this problem. We review efforts to redesign aaRSs, studies that designed new proteins and functionalities with the help of ncAAs, and some of the method developments that have been used, such as adaptive landscape flattening Monte Carlo, which allows an enzyme to be redesigned with substrate or transition state binding as the design target. [ABSTRACT FROM AUTHOR]

For the high throughput design of protein:peptide binding, one must explore a vast space of amino acid sequences in search of low binding free energies. This complex problem is usually addressed with either simple heuristic scoring or expensive sequence enumeration schemes. Far more efficient than enumeration is a recent Monte Carlo approach that adaptively flattens the energy landscape in sequence space of the unbound peptide and provides formally exact binding free energy differences. The method allows the binding free energy to be used directly as the design criterion. We propose several improvements that allow still more efficient sampling and can address larger design problems. They include the use of Replica Exchange Monte Carlo and landscape flattening for both the unbound and bound peptides. We used the method to design peptides that bind to the PDZ domain of the Tiam1 signaling protein and could serve as inhibitors of its activity. Four peptide positions were allowed to mutate freely. Almost 75 000 peptide variants were processed in two simulations of 109 steps each that used 1 CPU hour on a desktop machine. 96% of the theoretical sequence space was sampled. The relative binding free energies agreed qualitatively with values from experiment. The sampled sequences agreed qualitatively with an experimental library of Tiam1-binding peptides. The main assumption limiting accuracy is the fixed backbone approximation, which could be alleviated in future work by using increased computational resources and multi-backbone designs. [ABSTRACT FROM AUTHOR]

Research has been conducted on the molecular dynamics simulations of the biomolecular free energy differences. The use of the molecular dynamics and Poisson-Boltzmann methods with the amino acid recognition study carried out via aspartyl-tRNA synthetase is described.

The comparison of detailed atomistic simulations with simpler models such as a dielectric continuum model is useful in obtaining qualitative interpretation of the microscopic protein dynamics. The dielectric relaxation was found to involve both ligand and side chain rearrangements in the active site and accounted for a large part of overall charging free charge.

Molecular dynamics simulations were performed to determine the dielectric constants of four proteins in water, namely, performed myoglobin, apomyoglobin, the 60-residue B fragment of staphylococcal protein A, and the 56-residue immunoglobin-binding domain of streptococcal protein G. The dielectric constant, which represents the proteins' polarization response to a charged reaction, were found to be similar in value to those of ferro- and ferricytochrome c.

The movement of the test charge from the protein center to its surface shows that the relaxation free energies increase smoothly by a factor of 2. The relaxation is least at the heme center which accurately corresponds to the lower reorganization of free energy for electron transfers to and from the heme required biologically. Using linear response and thermodynamic perturbation theory, microscopic charge screening by the electron transfer protein cytochrome c in solution is examined.

Three mutant S-proteins were used in the simulation of hydrophobic interactions of peptide and protein components of the ribonuclease-S-complex. The free energies of binding for the three mutant proteins were calculated to approximate changes in binding energy. Results indicate that the alterations arising from mutations are limited to areas adjacent to the mutation site. Although the solvation of partial charges are equal for the three mutants and the native complex, changes in entropy and enthalpy cannot be gauged with certainty.