pStab: prediction of stable mutants, unfolding curves, stability maps and protein electrostatic frustration

Macromolecular crowding impact on anti-CRISPR AcrIIC3/NmeCas9 complex: Insights from scaled particle theory, molecular dynamics, and elastic networks models

The coupling of Cas9 and its inhibitor AcrIIC3, both from the bacterium Neisseria meningitidis (Nme), form a homodimer of the (NmeCas9/AcrIIC3)₂ type. This coupling was studied to assess the impact of their interaction with the crowders in the following environments: (1) homogeneous crowded, (2) heterogeneous, and (3) microheterogeneous cytoplasmic. For this, statistical thermodynamic models based on the scaled particle theory (SPT) were used, considering the attractive and repulsive protein-crowders contributions and the stability of the formation of spherocylindrical homodimers and the effects of changes in the size of spherical dimers were estimated. Studies based on models of dynamics, elastic networks, and statistical potentials to the formation of complexes NmeCas9/AcrIIC3 using PEG as the crowding agent support the predictions from SPT. Macromolecular crowding stabilizes the formation of the dimers, being more significant when the attractive protein-crowder interactions are weaker and the crowders are smaller. The coupling is favored towards the formation of spherical and compact dimers due to crowding addition (excluded-volume effects) and the thermodynamic stability of the dimers is markedly dependent on the size of the crowders. These results support the experimental mechanistic proposal of inhibition of NmeCas9 mediated by AcrIIC3.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments

Directed protein evolution applies repeated rounds of genetic mutagenesis and phenotypic screening and is often limited by experimental throughput. Through in silico prioritization of mutant sequences, machine learning has been applied to reduce wet lab burden to a level practical for human researchers. On the other hand, robotics permits large batches and rapid iterations for protein engineering cycles, but such capacities have not been well exploited in existing machine learning-assisted directed evolution approaches. Here, we report a scalable and batched method, Bayesian Optimization-guided EVOlutionary (BO-EVO) algorithm, to guide multiple rounds of robotic experiments to explore protein fitness landscapes of combinatorial mutagenesis libraries. We first examined various design specifications based on an empirical landscape of protein G domain B1. Then, BO-EVO was successfully generalized to another empirical landscape of an Escherichia coli kinase PhoQ, as well as simulated NK landscapes with up to moderate epistasis. This approach was then applied to guide robotic library creation and screening to engineer enzyme specificity of RhlA, a key biosynthetic enzyme for rhamnolipid biosurfactants. A 4.8-fold improvement in producing a target rhamnolipid congener was achieved after examining less than 1% of all possible mutants after four iterations. Overall, BO-EVO proves to be an efficient and general approach to guide combinatorial protein engineering without prior knowledge.

The Wako-Saitô-Muñoz-Eaton Model for Predicting Protein Folding and Dynamics

Despite the recent advances in the prediction of protein structures by deep neutral networks, the elucidation of protein-folding mechanisms remains challenging. A promising theory for describing protein folding is a coarse-grained statistical mechanical model called the Wako-Saitô-Muñoz-Eaton (WSME) model. The model can calculate the free-energy landscapes of proteins based on a three-dimensional structure with low computational complexity, thereby providing a comprehensive understanding of the folding pathways and the structure and stability of the intermediates and transition states involved in the folding reaction. In this review, we summarize previous and recent studies on protein folding and dynamics performed using the WSME model and discuss future challenges and prospects. The WSME model successfully predicted the folding mechanisms of small single-domain proteins and the effects of amino-acid substitutions on protein stability and folding in a manner that was consistent with experimental results. Furthermore, extended versions of the WSME model were applied to predict the folding mechanisms of multi-domain proteins and the conformational changes associated with protein function. Thus, the WSME model may contribute significantly to solving the protein-folding problem and is expected to be useful for predicting protein folding, stability, and dynamics in basic research and in industrial and medical applications.

The Physical Basis for pH Sensitivity in Biomolecular Structure and Function, With Application to the Spike Protein of SARS-CoV-2

Since pH sensitivity has a fundamental role in biology, much effort has been committed to establishing physical models to rationalize and predict pH dependence from molecular structures. Two of the key challenges are to accurately calculate ionizable group solvation and hydration and then to apply this modeling to all conformations relevant to the process in question. Explicit solvent methods coupled to molecular dynamics simulation are increasingly complementing lower resolution implicit solvent techniques, but equally, the scale of biological data acquisition leaves a role for high-throughput modeling. Additionally, determination of ranges of structures for a system allows sampling of key stages in solvation. In a review of the area, it is emphasized that pH sensors in biology beyond the most obvious candidate (histidine side chain, with an unshifted pK a near neutral pH) should be considered; that modeling can benefit from other concepts in bioinformatics, in particular modulation of interactions and function in families of homologs; and that it can also be beneficial to incorporate as many experimental structures as possible, to mitigate against small variations in conformation and to analyze larger, functional, conformational changes. These aspects are then demonstrated with new work on the spike protein of SARS-CoV-2, looking at the pH dependence of variants, including prediction of a change in the balance of locked, closed, and open forms at neutral pH for the Omicron variant spike protein.

Coarse-Grained Simulations of Protein Folding: Bridging Theory and Experiments

Computational coarse-grained models play a fundamental role as a research tool in protein folding, and they are important in bridging theory and experiments. Folding mechanisms are generally discussed using the energy landscape framework, which is well mapped within a class of simplified structure-based models. In this chapter, simplified computer models are discussed with special focus on structure-based ones.

Recent advances in user-friendly computational tools to engineer protein function

Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein-protein and protein-nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts.

Web-based tools for computational enzyme design

Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.

Predicting the stability of mutant proteins by computational approaches: an overview

A very large number of computational methods to predict the change in thermodynamic stability of proteins due to mutations have been developed during the last 30 years, and many different web servers are currently available. Nevertheless, most of them suffer from severe drawbacks that decrease their general reliability and, consequently, their applicability to different goals such as protein engineering or the predictions of the effects of mutations in genetic diseases. In this review, we have summarized all the main approaches used to develop these tools, with a survey of the web servers currently available. Moreover, we have also reviewed the different assessments made during the years, in order to allow the reader to check directly the different performances of these tools, to select the one that best fits his/her needs, and to help naïve users in finding the best option for their needs.

pPerturb: A Server for Predicting Long-Distance Energetic Couplings and Mutation-Induced Stability Changes in Proteins via Perturbations

The strength of intraprotein interactions or contact network is one of the dominant factors determining the thermodynamic stabilities of proteins. The nature and the extent of connectivity of this network also play a role in allosteric signal propagation characteristics upon ligand binding to a protein domain. Here, we develop a server for rapid quantification of the strength of an interaction network by employing an experimentally consistent perturbation approach previously validated against a large data set of 375 mutations in 19 different proteins. The web server can be employed to predict the extent of destabilization of proteins arising from mutations in the protein interior in experimentally relevant units. Moreover, coupling distances-a measure of the extent of percolation on perturbation-and overall perturbation magnitudes are predicted in a residue-specific manner, enabling a first look at the distribution of energetic couplings in a protein or its changes upon ligand binding. We show specific examples of how the server can be employed to probe for the distribution of local stabilities in a protein, to examine changes in side chain orientations or packing before and after ligand binding, and to predict changes in stabilities of proteins upon mutations of buried residues. The web server is freely available at http://pbl.biotech.iitm.ac.in/pPerturb and supports recent versions of all major browsers.

The Role of Electrostatics and Folding Kinetics on the Thermostability of Homologous Cold Shock Proteins

Understanding which aspects contribute to the thermostability of proteins is a challenge that has persisted for decades, and it is of great relevance for protein engineering. Several types of interactions can influence the thermostability of a protein. Among them, the electrostatic interactions have been a target of particular attention. Aiming to explore how this type of interaction can affect protein thermostability, this paper investigated four homologous cold shock proteins from psychrophilic, mesophilic, thermophilic, and hyperthermophilic organisms using a set of theoretical methodologies. It is well-known that electrostatics as well as hydrophobicity are key-elements for the stabilization of these proteins. Therefore, both interactions were initially analyzed in the native structure of each protein. Electrostatic interactions present in the native structures were calculated with the Tanford-Kirkwood model with solvent accessibility, and the amount of hydrophobic surface area buried upon folding was estimated by measuring both folded and extended structures. On the basis of Energy Landscape Theory, the local frustration and the simplified alpha-carbon structure-based model were modeled with a Debye-Hückel potential to take into account the electrostatics and the effects of an implicit solvent. Thermodynamic data for the structure-based model simulations were collected and analyzed using the Weighted Histogram Analysis and Stochastic Diffusion methods. Kinetic quantities including folding times, transition path times, folding routes, and Φ values were also obtained. As a result, we found that the methods are able to qualitatively infer that electrostatic interactions play an important role on the stabilization of the most stable thermophilic cold shock proteins, showing agreement with the experimental data.

TKSA-MC: A web server for rational mutation through the optimization of protein charge interactions

The TKSAMC is a web server which calculates protein charge-charge interactions via the Tanford-Kirkwood Surface Accessibility model with the Monte Carlo method for sampling different protein protonation states. The optimization of charge-charge interactions via directed mutations has successfully enhanced the thermal stability of different proteins and could be a key to protein engineering improvement. The server presents the electrostatic free energy contribution of each polar-charged residue to the protein native state stability. Specific residues are suggested to be mutated for improving thermal stability. The choice of a residue is based on its fraction of side chain exposed to solvent and its positive free energy contribution, which tends to destabilize the protein native state. Any residue energy contribution can be shown as a function of pH condition. The web server is freely available at UNESP (São Paulo State University - DF/IBILCE): http://tksamc.df.ibilce.unesp.br and also on GitHub https://github.com/contessoto/tksamc.