MOTIVATION: After the outstanding breakthrough of AlphaFold in predicting protein 3D models, new questions appeared and remain unanswered. The ensemble nature of proteins, for example, challenges the structural prediction methods because the models should represent a set of conformers instead of single structures. The evolutionary and structural features captured by effective deep learning techniques may unveil the information to generate several diverse conformations from a single sequence. Here, we address the performance of AlphaFold2 predictions obtained through ColabFold under this ensemble paradigm. RESULTS: Using a curated collection of apo-holo pairs of conformers, we found that AlphaFold2 predicts the holo form of a protein in ∼70% of the cases, being unable to reproduce the observed conformational diversity with the same error for both conformers. More importantly, we found that AlphaFold2's performance worsens with the increasing conformational diversity of the studied protein. This impairment is related to the heterogeneity in the degree of conformational diversity found between different members of the homologous family of the protein under study. Finally, we found that main-chain flexibility associated with apo-holo pairs of conformers negatively correlates with the predicted local model quality score plDDT, indicating that plDDT values in a single 3D model could be used to infer local conformational changes linked to ligand binding transitions. AVAILABILITY AND IMPLEMENTATION: Data and code used in this manuscript are publicly available at https://gitlab.com/sbgunq/publications/af2confdiv-oct2021. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: ASES is a versatile tool for assessing the impact of alternative splicing (AS), initiation and termination of transcription on protein diversity in evolution. It identifies exon and transcript orthogroups from a set of input genes/species for comparative transcriptomics analyses. It computes an evolutionary splicing graph, where the nodes are exon orthogroups, allowing for a direct evaluation of AS conservation. It also reconstructs a transcripts' phylogenetic forest to date the appearance of specific transcripts and explore the events that have shaped them. ASES web server features a highly interactive interface enabling the synchronous selection of events, exons or transcripts in the different outputs, and the visualization and retrieval of the corresponding amino acid sequences, for subsequent 3D structure prediction. AVAILABILITY AND IMPLEMENTATION: http://www.lcqb.upmc.fr/Ases. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Major scientific challenges that are beyond the capability of individuals need to be addressed by multi-disciplinary and multi-institutional consortia. Examples of these endeavours include the Human Genome Project, and more recently, the Structural Genomics (SG) initiative. The SG initiative pursues the expansion of structural coverage to include at least one structural representative for each protein family to derive the remaining structures using homology modelling. However, biological function is inherently connected with protein dynamics that can be studied by knowing different structures of the same protein. This ensemble of structures provides snapshots of protein conformational diversity under native conditions. Thus, sequence redundancy in the Protein Data Bank (PDB) (i.e. crystallization of the same protein under different conditions) is therefore an essential input contributing to experimentally based studies of protein dynamics and providing insights into protein function. In this work, we show that sequence redundancy, a key concept for exploring protein dynamics, is highly biased and fundamentally incomplete in the PDB. Additionally, our results show that dynamical behaviour of proteins cannot be inferred using homologous proteins. Minor to moderate changes in sequence can produce great differences in dynamical behaviour. Nonetheless, the structural and dynamical incompleteness of the PDB is apparently unrelated concepts in SG. While the first could be reversed by promoting the extension of the structural coverage, we would like to emphasize that further focused efforts will be needed to amend the incompleteness of the PDB in terms of dynamical information content, essential to fully understand protein function.

Motivation: MIToS is an environment for mutual information analysis and a framework for protein multiple sequence alignments (MSAs) and protein structures (PDB) management in Julia language. It integrates sequence and structural information through SIFTS, making Pfam MSAs analysis straightforward. MIToS streamlines the implementation of any measure calculated from residue contingency tables and its optimization and testing in terms of protein contact prediction. As an example, we implemented and tested a BLOSUM62-based pseudo-count strategy in mutual information analysis. Availability and Implementation: The software is totally implemented in Julia and supported for Linux, OS X and Windows. It's freely available on GitHub under MIT license: http://mitos.leloir.org.ar . Contacts: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Protein motions are a key feature to understand biological function. Recently, a large-scale analysis of protein conformational diversity showed a positively skewed distribution with a peak at 0.5 Å C-alpha root-mean-square-deviation (RMSD). To understand this distribution in terms of structure-function relationships, we studied a well curated and large dataset of ~5,000 proteins with experimentally determined conformational diversity. We searched for global behaviour patterns studying how structure-based features change among the available conformer population for each protein. This procedure allowed us to describe the RMSD distribution in terms of three main protein classes sharing given properties. The largest of these protein subsets (~60%), which we call "rigid" (average RMSD = 0.83 Å), has no disordered regions, shows low conformational diversity, the largest tunnels and smaller and buried cavities. The two additional subsets contain disordered regions, but with differential sequence composition and behaviour. Partially disordered proteins have on average 67% of their conformers with disordered regions, average RMSD = 1.1 Å, the highest number of hinges and the longest disordered regions. In contrast, malleable proteins have on average only 25% of disordered conformers and average RMSD = 1.3 Å, flexible cavities affected in size by the presence of disordered regions and show the highest diversity of cognate ligands. Proteins in each set are mostly non-homologous to each other, share no given fold class, nor functional similarity but do share features derived from their conformer population. These shared features could represent conformational mechanisms related with biological functions.