**5. Highlights**

*Monoclonal Antibodies*

earlier, e.g., molecular dynamics.

aggregation, related to colloidal stability.

cial antibodies, without correlation with 3D structures.

development/discovery of new therapeutic drugs.

between affinity and stability during antibody engineering (**Table 1**). Before proceeding to available resources to deal with the destabilizing process, we should mention two types of stability in antibodies: physical and chemical. The physical stability of a protein is related to conformational changes and also to its colloidal stability. Concerning the conformational changes, we relate the free energy (ΔG) of the protein in its unfolded and folded-state, and the folded-state should present less energy than the unfolded state (Gfolded-state < Gunfolded-state) [121]. One of the *in silico* methods used to investigate folded and unfolded-state energies was mentioned

Among the numerous *in silico* tools for predicting conformational stability, DeepDDG [45] proved to be quite efficient compared to eight other methods (**Table 1**). DeepDGG is a machine learning method trained from 5444 experimental data. This tool allows the calculation of the energy difference between the mutated protein and its native state. This calculation allows us to observe whether the proposed mutations, for example, for an improvement in affinity, cause structure destabilization. Experimentally, the conformational change of a protein it is accessed indirectly through its melting temperature (Tm), and it can be measured by different experimental techniques, such as scanning calorimetry (DSC), differential scanning fluorometry (DSF), and circular dichroism (CD). The changes between folded and unfolded-state can be reversible, unlike the process known as

Although aggregation is different from solubility, the solubility of a molecule is usually calculated for aggregation prediction. In computational chemistry, aggregation and solubility are commonly treated as the same parameter. The aggregation tendency of some mAbs that could impair their efficacy might be prevented through aggregation-prone regions (APRs) analyses. APR assays rely on the hydrophobicity scales and residues' charge annotations. Among several predictors of solubility and APRs for proteins, it is possible to highlight two endeavors successfully applied to antibodies: Wang et al*.* [122] combined tools to predict APRs in commercial mAbs. They found similar aggregation-prone motifs among commercial and non-commer-

In 2011, Agrawal et al*.* [123] compared several aggregation prediction tools demonstrating their usefulness in drug discovery and development, especially when screening a large number of molecules by fast and low cost *in silico* assays. Recently, Raybould et al. [53] launched Therapeutic Antibody Profiler (TAP), a web application that compares candidates' sequences with natural antibody sequences, as natural antibodies are assumed to display favorable biophysical properties. TAP, notably, depends on the previous data of clinical-stage antibody therapeutics (CST). So, the robustness of this method is directly affected by the input improvement of the CST database. One modern and elegant approach drove the development of AggreRATE-Disc [54], a machine learning-based tool that can predict, within the sequences, mutations that can promote or mitigate aggregation. Although *in silico* tools can highlight sequences with aggregation issues, they do not substitute experimental assays; however, they can be managed, reducing the totality of necessary tests. These tools and databases help the screening steps across the

Regarding the chemical stability of antibodies, it is possible to mention the degradation by chemical modification of amino acids, such as asparagine (Asn) deamidation, aspartate (Asp) isomerization, methionine (Met) oxidation, and lysine (Lys) glycation [124, 125]. The IgGs are commonly N-glycosylated at Asp297 residue in each Fc-CH2 domain [126]. These Fc N-glycan are associated with correct

**116**

Advances in bioinformatics allow us to outline different strategies in the discovery of new therapeutic antibodies. There has been significant progress in online tools in recent years, and probably the refinement of the techniques will be increased, bringing more accurate and reliable results.

Online platforms can present a long wait and execution times. The use of those platforms requires a good internet connection, and also a robust computer for analysis and treatment of the generated data.

Bioinformatics is a notably promising field, and indeed, has a prominent place on the innovation frontier.
