In Silico Generation of Structural and Intermolecular Binding Affinity Data with Reasonable Accuracy: Expanding Horizons in Drug Discovery and Design

Abstract

In the domain of drug discovery and design, the acquisition of precise structural and intermolecular Kd data for biological molecules is paramount. However, conventional methods for obtaining such data are often beset by challenges including high costs, time intensiveness, and inherent limitations in scalability and diversity. This article puts forward a high-throughput approach for in silico generation of structural and intermolecular binding affinity (Kd) data, which keeps exploring the uncharted territories of drug discovery & design by harnessing computational methodologies to synthetically generate diverse datasets. Through a meticulously designed workflow encompassing molecular structural modeling, structural biophysics-based calculations of intermolecular binding affinity (Kd), this innovative methodology transcends the constraints of traditional experimentation, offering a cost-effective, scalable, and efficient alternative. By simulating molecular interactions and binding interfaces and predicting binding affinities with reasonable accuracy, this approach not only expedites the drug development process but also enables the exploration of vast molecular space, thereby facilitating the discovery of novel therapeutics beyond conventional drug modality as a form factor. Moreover, the versatility of synthetic data extends beyond virtual screening and lead optimization, encompassing applications such as dataset augmentation, model validation, and benchmarking against experimental data. This article elucidates the conceptual underpinnings, methodological intricacies, validation strategies, and potential ramifications of in silico generated data, heralding a paradigm shift in drug discovery paradigms. By fostering synergy between computational and experimental research domains, this innovative approach promises to accelerate the pace of drug discovery, enhance the robustness of predictive models, and pave the way for transformative advancements in the entire pharmaceutical industry.

A one-dimensional semaglutide-GLP-1R-based mini static GIBAC

“…With PDB entry 4ZGM [42] (Table 2) as an initial input and an automated in silico generation of synthetic structural and K d data [60], this article for the first time puts forward a prototype GIBAC, i.e., semaGIBAC, for which everything is included in Table S1 of supplementary file semaGIBAC.pdf:…”

“…First, with PDB entry 4ZGM [42] (Table 2) in place, Modeller [50] was employed to build 10000 structural models with 100% homology to PDB ID: 4ZGM [42], the binding affinities between semaglutide and GLP-1R were calculated using Prodigy [57,58] 10000 times [59]. Second, with PDB entry 4ZGM [42] (Table 2) in place as an initial input, the process of the construction of a prototype GIBAC (semaGIBAC, Equation 1) subsequently consists of an automated in silico generation of synthetic structural and K d data, as illustrated in Figure 2 and described previously in detail [60]. Briefly, Modeller [50] was employed to build a total of 11200 ( 28!…”

“…In this regard, this article puts forward a set of in silico steps of structural and biophysical data generation towards a paradigm shift in precise drug discovery and design [59,60]. Take semaglutide for instance, a five-dimensional semaGIBAC requires a total of 314496000000 (Table 5) homology structural models with 82.14% (23/28) homology to PDB ID: 4ZGM [42] to be built by Modeller [50], and subsequently a total of 314496000000 (Table 5) times of Prodigy-based [57,58] calculations of the binding affinities between semaglutide analogues and GLP-1R.…”

“…Therefore, this article here again proposes an AI-based open strategy [95] to make it possible and conceivable to generate a vast amount of data to train a real GIBAC with reasonable accuracy and precision, as openness in data acquisition and generation, and AI algorithms is essential for promoting transparency, reproducibility, and collaboration within the community of drug discovery & design, and for facilitating continued improvement of the performance (accuracy, precision and efficiency) of GIBAC in precise drug discovery & design [35,36] in future. As mentioned above, this article puts forward a set of in silico steps of structural and biophysical data generation towards a paradigm shift in precise drug discovery and design [59,60]. Specifically, here, the generation of synthetic structural and K d data is akin to the distribution of the electron cloud of a hydrogen atom (Figure 4), where Radius is the distance between the electron (synthetic data) and the proton (i.e., nucleus, experimental data) of the hydrogen atom.…”

Optimizing GLP-1R Agonist: A Computational Semaglutide Analogue with 112-fold Enhanced Binding Affinity to GLP-1R

“…Briefly, the amino acid sequences of the two chains of semaglutide and GLP-1R (according to PDB entry 4ZGM [44]) are listed in italics in fasta format as below, >4ZGM_1|Chain A|Glucagon-like peptide 1 receptor|Homo sapiens (9606) RPQGATVSLWETVQKWREYRRQCQRSLTEDPPPATDLFCNRTFDEYACWPDGEPGSFVNVSC PWYLPWASSVPQGHVYRFCTAEGLWLQKDNSSLPWRDLSECEESKRGERSSPEEQLLFLY >4ZGM_2|Chain B|Semaglutide peptide backbone; 8Aib,34R-GLP-1(7-37)-OH|Homo sapiens (9606) HAEGTFTSDVSSYLEGQAAKEFIAWLVRGRG First, with PDB entry 4ZGM [44] (Table 3) in place, Modeller [51] was employed to build 10000 structural models with 100% homology to PDB ID: 4ZGM [44], the binding affinities between semaglutide and GLP-1R were calculated using Prodigy [52,53] 10000 times [54]. Second, with PDB entry 4ZGM [44] (Table 3) in place as an initial input, the process of the construction of a prototype GIBAC (semaGIBAC [24]) subsequently consists of an automated in silico generation of synthetic structural and K d data, as illustrated in Figure 3 and described previously in detail [55]. Briefly, Modeller [51] was employed to build a total of 11200 ( 28!…”