LoopIng: A template-based tool for predicting the structure of protein loops

MOTIVATION: Predicting the structure of protein loops is very challenging, mainly because they are not necessarily subject to strong evolutionary pressure. This implies that, unlike the rest of the protein, standard homology modeling techniques are not very effective in modeling their structure. However, loops are often involved in protein function, hence inferring their structure is important for predicting protein structure as well as function. RESULTS: We describe a method, LoopIng, based on the Random Forest automated learning technique, which, given a target loop, selects a structural template for it from a database of loop candidates. Compared to the most recently available methods, LoopIng is able to achieve similar accuracy for short loops (4-10 residues) and significant enhancements for long loops (11-20 residues). The quality of the predictions is robust to errors that unavoidably affect the stem regions when these are modeled. The method returns a confidence score for the predicted template loops and has the advantage of being very fast (on average: 1 min/loop)

A Self-Organizing Algorithm for Modeling Protein Loops

Protein loops, the flexible short segments connecting two stable secondary structural units in proteins, play a critical role in protein structure and function. Constructing chemically sensible conformations of protein loops that seamlessly bridge the gap between the anchor points without introducing any steric collisions remains an open challenge. A variety of algorithms have been developed to tackle the loop closure problem, ranging from inverse kinematics to knowledge-based approaches that utilize pre-existing fragments extracted from known protein structures. However, many of these approaches focus on the generation of conformations that mainly satisfy the fixed end point condition, leaving the steric constraints to be resolved in subsequent post-processing steps. In the present work, we describe a simple solution that simultaneously satisfies not only the end point and steric conditions, but also chirality and planarity constraints. Starting from random initial atomic coordinates, each individual conformation is generated independently by using a simple alternating scheme of pairwise distance adjustments of randomly chosen atoms, followed by fast geometric matching of the conformationally rigid components of the constituent amino acids. The method is conceptually simple, numerically stable and computationally efficient. Very importantly, additional constraints, such as those derived from NMR experiments, hydrogen bonds or salt bridges, can be incorporated into the algorithm in a straightforward and inexpensive way, making the method ideal for solving more complex multi-loop problems. The remarkable performance and robustness of the algorithm are demonstrated on a set of protein loops of length 4, 8, and 12 that have been used in previous studies

The structures of secretory and dimeric immunoglobulin A

Secretory (S) Immunoglobulin (Ig) A is the predominant mucosal antibody, which binds pathogens and commensal microbes. SIgA is a polymeric antibody, typically containing two copies of IgA that assemble with one joining-chain (JC) to form dimeric (d) IgA that is bound by the polymeric Ig-receptor ectodomain, called secretory component (SC). Here, we report the cryo-electron microscopy structures of murine SIgA and dIgA. Structures reveal two IgAs conjoined through four heavy-chain tailpieces and the JC that together form a β-sandwich-like fold. The two IgAs are bent and tilted with respect to each other, forming distinct concave and convex surfaces. In SIgA, SC is bound to one face, asymmetrically contacting both IgAs and JC. The bent and tilted arrangement of complex components limits the possible positions of both sets of antigen-binding fragments (Fabs) and preserves steric accessibility to receptor-binding sites, likely influencing antigen binding and effector functions