Homology modeling in the time of collective and artificial intelligence

Abstract

Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.

Peroxide-Mediated Oxygenation of Organic Compounds by Fungal Peroxygenases

“…Meanwhile, 3D modeling based on primary sequences and known crystal structures has reached such quality that it can be used to obtain reliable protein structures [ 83 , 84 ].…”

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

“…Obtaining structures from x-ray crystallography or cryo-EM, while less straightforward than for the case of water-soluble proteins, is still possible for integral membrane proteins within membrane-like nanodiscs [904][905][906] or a detergent environment [907][908][909]; obtaining such information for the more weakly associated proteins directly from experiment is challenging and rarely achieved. The rare cases of success in this regard represent a Herculean effort: one must work with incomplete structures combining fragments originating from several PDB entries and, frequently, the assistance of theoretical methods is necessary (e.g., homology modeling [910][911][912] and modeling of short loops [913]). Moreover, extracellular domains of bitopic receptors are heavily glycosylated and the precise sequences of these complex carbohydrate branches have, in most cases, not been determined.…”

Artificial Intelligence in Translational Medicine

“…Interestingly, these approaches have provided potential drugs for treating CoVid-19 in a short time [61,62]. Currently, protein structural modeling and design as well as protein structure prediction, that can increase the proficiency in the drug discovery pipeline, are emerging areas of application of ML models [63][64][65][66]. In fact, ML methods offer a theoretical framework for identifying and prioritizing bioactive molecules possessing suitable pharmacological profile, as well as optimizing them as drug-like lead compounds before clinical investigation [58].…”