LB3D: a protein three-dimensional substructure search program based on the lower bound of a root mean square deviation value

NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation

RNA plays a crucial role not only in information transfer as messenger RNA during gene expression but also in various biological functions as non-coding RNAs. Understanding mechanical mechanisms of function needs tertiary structure information; however, experimental determination of three-dimensional RNA structures is costly and time-consuming, leading to a substantial gap between RNA sequence and structural data. To address this challenge, we developed NuFold, a novel computational approach that leverages state-of-the-art deep learning architecture to accurately predict RNA tertiary structures. NuFold is a deep neural network trained end-to-end for the output structure from the input sequence. NuFold incorporates a nucleobase center representation, which enables flexible conformation of ribose rings. Benchmark study showed that NuFold clearly outperformed energy-based methods and demonstrated comparable results with existing state-of-the-art deep-learning-based methods. NuFold exhibited a particular advantage in building correct local geometries of RNA. Analyses of individual components in the NuFold pipeline indicated that the performance improved by utilizing metagenome sequences for multiple sequence alignment and increasing the number of recycling. NuFold is also capable of predicting multimer complex structures of RNA by linking the input sequences.

RL-MLZerD: Multimeric protein docking using reinforcement learning

Numerous biological processes in a cell are carried out by protein complexes. To understand the molecular mechanisms of such processes, it is crucial to know the quaternary structures of the complexes. Although the structures of protein complexes have been determined by biophysical experiments at a rapid pace, there are still many important complex structures that are yet to be determined. To supplement experimental structure determination of complexes, many computational protein docking methods have been developed; however, most of these docking methods are designed only for docking with two chains. Here, we introduce a novel method, RL-MLZerD, which builds multiple protein complexes using reinforcement learning (RL). In RL-MLZerD a multi-chain assembly process is considered as a series of episodes of selecting and integrating pre-computed pairwise docking models in a RL framework. RL is effective in correctly selecting plausible pairwise models that fit well with other subunits in a complex. When tested on a benchmark dataset of protein complexes with three to five chains, RL-MLZerD showed better modeling performance than other existing multiple docking methods under different evaluation criteria, except against AlphaFold-Multimer in unbound docking. Also, it emerged that the docking order of multi-chain complexes can be naturally predicted by examining preferred paths of episodes in the RL computation.

CAB-Align: A Flexible Protein Structure Alignment Method Based on the Residue-Residue Contact Area

Proteins are flexible, and this flexibility has an essential functional role. Flexibility can be observed in loop regions, rearrangements between secondary structure elements, and conformational changes between entire domains. However, most protein structure alignment methods treat protein structures as rigid bodies. Thus, these methods fail to identify the equivalences of residue pairs in regions with flexibility. In this study, we considered that the evolutionary relationship between proteins corresponds directly to the residue–residue physical contacts rather than the three-dimensional (3D) coordinates of proteins. Thus, we developed a new protein structure alignment method, contact area-based alignment (CAB-align), which uses the residue–residue contact area to identify regions of similarity. The main purpose of CAB-align is to identify homologous relationships at the residue level between related protein structures. The CAB-align procedure comprises two main steps: First, a rigid-body alignment method based on local and global 3D structure superposition is employed to generate a sufficient number of initial alignments. Then, iterative dynamic programming is executed to find the optimal alignment. We evaluated the performance and advantages of CAB-align based on four main points: (1) agreement with the gold standard alignment, (2) alignment quality based on an evolutionary relationship without 3D coordinate superposition, (3) consistency of the multiple alignments, and (4) classification agreement with the gold standard classification. Comparisons of CAB-align with other state-of-the-art protein structure alignment methods (TM-align, FATCAT, and DaliLite) using our benchmark dataset showed that CAB-align performed robustly in obtaining high-quality alignments and generating consistent multiple alignments with high coverage and accuracy rates, and it performed extremely well when discriminating between homologous and nonhomologous pairs of proteins in both single and multi-domain comparisons. The CAB-align software is freely available to academic users as stand-alone software at http://www.pharm.kitasato-u.ac.jp/bmd/bmd/Publications.html.

Finding All Longest Common Segments in Protein Structures Efficiently

The Local/Global Alignment (Zemla, 2003), or LGA, is a popular method for the comparison of protein structures. One of the two components of LGA requires us to compute the longest common contiguous segments between two protein structures. That is, given two structures A = (a1, ... ,a(n)) and B = (b1, ... ,b(n)) where a(k), b(k) ∈ ℝ(3), we are to find, among all the segments f = (a(i), ... ,a(j)) and g = (b(i), ... ,b(j)) that fulfill a certain criterion regarding their similarity, those of the maximum length. We consider the following criteria: (1) the root mean squared deviation (RMSD) between f and g is to be within a given t ∈ ℝ; (2) f and g can be superposed such that for each k, i ≤ k ≤ j, ||a(k) - b(k)|| ≤ t for a given t ∈ ℝ. We give an algorithm of O(n log n + nl) time complexity when the first requirement applies, where l is the maximum length of the segments fulfilling the criterion. We show an FPTAS which, for any ϵ ∈ ℝ, finds a segment of length at least l, but of RMSD up to (1 + ϵ)t, in O(n log n + n/ϵ) time. We propose an FPTAS which for any given ϵ ∈ R, finds all the segments f and g of the maximum length which can be superposed such that for each k, i ≤ k ≤ j, ||a(k) - b(k)|| ≤ (1 + ϵ)t, thus fulfilling the second requirement approximately. The algorithm has a time complexity of O(n log(2) n/ϵ(5)) when consecutive points in A are separated by the same distance (which is the case with protein structures). These worst-case runtime complexities are verified using C++ implementations of the algorithms, which we have made available at http://alcs.sourceforge.net/.