MOTIVATION: Peptide therapeutics hinge on the precise interaction between a tailored peptide and its designated receptor while mitigating interactions with alternate receptors is equally indispensable. Existing methods primarily estimate the binding score between protein and peptide pairs. However, for a specific peptide without a corresponding protein, it is challenging to identify the proteins it could bind due to the sheer number of potential candidates. RESULTS: We propose a transformers-based protein embedding scheme in this study that can quickly identify and rank millions of interacting proteins. Furthermore, the proposed approach outperforms existing sequence- and structure-based methods, with a mean AUC-ROC and AUC-PR of 0.73. AVAILABILITY AND IMPLEMENTATION: Training data, scripts, and fine-tuned parameters are available at https://github.com/RoniGurvich/Peptriever. The proposed method is linked with a web application available for customized prediction at https://peptriever.app/.

MOTIVATION: The recent emergence of the novel SARS-coronavirus 2 (SARS-CoV-2) and its international spread pose a global health emergency. The spike (S) glycoprotein binds ACE2 and promotes SARS-CoV-2 entry into host cells. The trimeric S protein binds the receptor using the receptor-binding domain (RBD) causing conformational changes in S protein that allow priming by host cell proteases. Unraveling the dynamic structural features used by SARS-CoV-2 for entry might provide insights into viral transmission and reveal novel therapeutic targets. Using structures determined by X-ray crystallography and cryo-EM, we performed structural analysis and atomic comparisons of the different conformational states adopted by the SARS-CoV-2-RBD. RESULTS: Here, we determined the key structural components induced by the receptor and characterized their intramolecular interactions. We show that κ-helix (polyproline-II) is a predominant structure in the binding interface and in facilitating the conversion to the active form of the S protein. We demonstrate a series of conversions between switch-like κ-helix and β-strand, and conformational variations in a set of short α-helices which affect the hinge region. These conformational changes lead to an alternating pattern in conserved disulfide bond configurations positioned at the hinge, indicating a possible disulfide exchange, an important allosteric switch implicated in viral entry of various viruses, including HIV and murine coronavirus. The structural information presented herein enables to inspect and understand the important dynamic features of SARS-CoV-2-RBD and propose a novel potential therapeutic strategy to block viral entry. Overall, this study provides guidance for the design and optimization of structure-based intervention strategies that target SARS-CoV-2. AVAILABILITY AND IMPLEMENTATION: We have implemented the proposed methods in an R package freely available at https://github.com/Grantlab/bio3d. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: More than half of the human proteome contains the proline-rich motif, PxxP. This motif has a high propensity for adopting a left-handed polyproline II (PPII) helix and can potentially bind SH3 domains. SH3 domains are generally grouped into two classes, based on whether the PPII binds in a positive (N-to-C terminal) or negative (C-to-N terminal) orientation. Since the discovery of this structural motif, over six decades ago, a systematic understanding of its binding remains poor and the consensus amino acid sequence that binds SH3 domains is still ill defined. RESULTS: Here, we show that the PPII interaction with SH3 domains is governed by the helix backbone and its prolines, and their rotation angle around the PPII helical axis. Based on a geometric analysis of 131 experimentally solved SH3 domains in complex with PPIIs, we observed a rotary translation along the helical screw axis, and separated them by 120° into three categories we name α (0-120°), β (120-240°) and γ (240-360°). Furthermore, we found that PPII helices are distinguished by a shifting PxxP motif preceded by positively charged residues which act as a structural reading frame and dictates the organization of SH3 domains; however, there is no one single consensus motif for all classified PPIIs. Our results demonstrate a remarkable apparatus of a lock with a rotating and translating key with no known equivalent machinery in molecular biology. We anticipate our model to be a starting point for deciphering the PPII code, which can unlock an exponential growth in our understanding of the relationship between protein structure and function. AVAILABILITY AND IMPLEMENTATION: We have implemented the proposed methods in the R software environment and in an R package freely available at https://github.com/Grantlab/bio3d. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Polyproline II (PPII) is a common conformation, comparable to α-helix and β-sheet. PPII, recently termed with a more generic name-κ-helix, adopts a left-handed structure with 3-fold rotational symmetry. Lately, a new type of binding mechanism-the helical lock and key model was introduced in SH3-domain complexes, where the interaction is characterized by a sliding helical pattern. However, whether this binding mechanism is unique only to SH3 domains is unreported. RESULTS: Here, we show that the helical binding pattern is a universal feature of the κ-helix conformation, present within all the major target families-SH3, WW, profilin, MHC-II, EVH1 and GYF domains. Based on a geometric analysis of 255 experimentally solved structures, we found that they are characterized by a distinctive rotational angle along the helical axis. Furthermore, we found that the range of helical pitch varies between different protein domains or peptide orientations and that the interaction is also represented by a rotational displacement mimicking helical motion. The discovery of rotational interactions as a mechanism, reveals a new dimension in the realm of protein-protein interactions, which introduces a new layer of information encoded by the helical conformation. Due to the extensive involvement of the conformation in functional interactions, we anticipate our model to expand the current molecular understanding of the relationship between protein structure and function. AVAILABILITY AND IMPLEMENTATION: We have implemented the proposed methods in an R package freely available at https://github.com/Grantlab/bio3d. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accurate prediction of active sites is an important tool in bioinformatics. Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique is trained using a small 133 enzyme dataset and tested using a large 845 enzyme dataset, both with known active site residues. EXPOSITE is also tested in a benchmark protein ligand dataset (PLD) comprising 48 proteins with and without bound ligands. EXPOSITE is shown to successfully locate the active site in most instances, and is found to be more accurate than other structure-based techniques. Interestingly, in several instances, the active site does not correspond to the largest pocket. EXPOSITE is advantageous due to its high precision and paves the way for structure based prediction of active site in enzymes.