Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein–ligand scoring function. In this review, we give a broad overview of recent scoring function development, as well as the docking-based applications in drug discovery. We outline the strategies and resources available for structure-based VS and discuss the assessment and development of classical and machine learning protein–ligand scoring functions. In particular, we highlight the recent progress of machine learning scoring function ranging from descriptor-based models to deep learning approaches. We also discuss the general workflow and docking protocols of structure-based VS, such as structure preparation, binding site detection, docking strategies, and post-docking filter/re-scoring, as well as a case study on the large-scale docking-based VS test on the LIT-PCBA data set.

The recent focus on protein-protein interaction networks has increasingly been shifted towards the disruption of protein complexes, which either are mediated by the binding of a globular domain in one protein to a short peptide stretch in another, or involve flat, large, and hydrophobic interfaces that classical small-molecule agents are not always ideally suited. Rational design of therapeutic peptides with high affinity targeting such interactions has emerged as a new and promising tool in discovery of potential drug candidates against associated diseases. The design is commonly based on bioinformatics methods or molecular modeling techniques, indirectly exploiting structure-activity relationship at the level of peptide sequence or directly deriving lead entities from protein complex architecture. Here, a newly rising subfield called computational peptidology that focuses on the use of computational and theoretical approaches to treat peptiderelated problems is comprehensively reviewed on the design and discovery of peptide agents targeting protein-protein interactions. We address a systematic discussion on several representative cases in which the computational peptidology is successfully employed to develop peptide therapeutics. Besides, some problems and pitfalls accompanied with the current use of computational methods in peptide modeling and design are also present.

Self-binding peptides (SBPs) represent a novel biomolecular phenomenon spanning between folding and binding, where a short peptide segment within a monomeric protein fulfills biological functions by dynamically binding to/unbinding from its target domain in the same monomer. Here, we were able to quantitatively reconstruct the complete structural dynamics picture of binding of free SBPs to their target domains for five representative SBP systems by carrying out the state-of-the-art molecular dynamics (MD) simulations. In the picture, a two-step binding mechanism for SBP–domain recognition and association was proposed, which includes a fast, nonspecific diffusive phase and a slow, specific organizational phase. The electrostatic interactions and desolvation effects play a predominant role in the first phase that leads to the formation of a metastable encounter complex, while conformational rearrangement is observed in the second phase to optimize the exquisite network of nonbonded chemical forces such as hydrogen bonds and salt bridges across the complex interface. From an energetic point of view, a funnel-shape enthalpy landscape steers these SBPs towards their native bound state and thus facilitates the binding process. However, the binding exhibits typical enthalpy–entropy compensation due to the high flexibility of peptides that results in a relatively low affinity for SBP–domain binding and forces the SBP systems to rapidly switch between the bound and unbound states. In addition, slight conformational changes in the target domain and/or in the polypeptide linker between the domain and peptide can significantly affect the energetic properties and dynamic behavior of the fine-tuned binding process of SBP–domain recognition.

Drug-induced adverse reactions are a significant problem in healthcare worldwide and are estimated to cost billions of dollars annually in the United States. A portion of such reactions is observed to strongly associate with certain human leukocyte antigen (HLA) alleles; one of the strongest associations is the HLA-B*1502 protein with carbamazepine (CBZ)-induced Stevens–Johnson syndrome/toxic epidermal necrolysis (SJS/TEN) – the odds ratio value can even be higher than one thousand. The particularly strong association in CBZ-induced SJS/TEN suggests that the HLA-B*1502 is not only a genetic marker but also a participant in the pathogenesis of the disease. In the current study, we attempt to computationally model the atomic-level structure of the complete HLA-B*1502/peptide/CBZ/T-cell receptor (TCR) complex architecture based on prior knowledge obtained from epidemiological investigations as well as in vitro and in vivo assays. The model tells a different story about the molecular mechanism of CBZ-induced SJS/TEN from that previously reported for abacavir (ABC)-induced hypersensitivity (HSR); the CBZ molecule is located at the interface between HLA-B*1502/peptide and TCR, directly contacts the P3–P6 residues of antigen peptide, and bound within a pocket region encompassed by two TCR CDR3 fingers. Molecular dynamics simulation and binding energy analysis further reveal that the CBZ shows considerably high affinity to TCR over HLA-B*1502/peptide, which can tightly interact with the former rather than the latter. From the model, two hypotheses are proposed that can well explain most previous observations and are expected to guide next wet-lab experiments. This study could help to promote our understanding of the molecular mechanism and pathological implication underlying CBZ-induced SJS/TEN.