Contrastive learning on protein embeddings enlightens midnight zone

Experimental structures are leveraged through multiple sequence alignments, or more generally through homology-based inference (HBI), facilitating the transfer of information from a protein with known annotation to a query without any annotation. A recent alternative expands the concept of HBI from sequence-distance lookup to embedding-based annotation transfer (EAT). These embeddings are derived from protein Language Models (pLMs). Here, we introduce using single protein representations from pLMs for contrastive learning. This learning procedure creates a new set of embeddings that optimizes constraints captured by hierarchical classifications of protein 3D structures defined by the CATH resource. The approach, dubbed ProtTucker, has an improved ability to recognize distant homologous relationships than more traditional techniques such as threading or fold recognition. Thus, these embeddings have allowed sequence comparison to step into the 'midnight zone' of protein similarity, i.e. the region in which distantly related sequences have a seemingly random pairwise sequence similarity. The novelty of this work is in the particular combination of tools and sampling techniques that ascertained good performance comparable or better to existing state-of-the-art sequence comparison methods. Additionally, since this method does not need to generate alignments it is also orders of magnitudes faster. The code is available at https://github.com/Rostlab/EAT

webPRC: the Profile Comparer for alignment-based searching of public domain databases

Profile–profile methods are well suited to detect remote evolutionary relationships between protein families. Profile Comparer (PRC) is an existing stand-alone program for scoring and aligning hidden Markov models (HMMs), which are based on multiple sequence alignments. Since PRC compares profile HMMs instead of sequences, it can be used to find distant homologues. For this purpose, PRC is used by, for example, the CATH and Pfam-domain databases. As PRC is a profile comparer, it only reports profile HMM alignments and does not produce multiple sequence alignments. We have developed webPRC server, which makes it straightforward to search for distant homologues or similar alignments in a number of domain databases. In addition, it provides the results both as multiple sequence alignments and aligned HMMs. Furthermore, the user can view the domain annotation, evaluate the PRC hits with the Jalview multiple alignment editor and generate logos from the aligned HMMs or the aligned multiple alignments. Thus, this server assists in detecting distant homologues with PRC as well as in evaluating and using the results. The webPRC interface is available at http://www.ibi.vu.nl/programs/prcwww/

COMPASS server for homology detection: improved statistical accuracy, speed and functionality

COMPASS is a profile-based method for the detection of remote sequence similarity and the prediction of protein structure. Here we describe a recently improved public web server of COMPASS, http://prodata.swmed.edu/compass. The server features three major developments: (i) improved statistical accuracy; (ii) increased speed from parallel implementation; and (iii) new functional features facilitating structure prediction. These features include visualization tools that allow the user to quickly and effectively analyze specific local structural region predictions suggested by COMPASS alignments. As an application example, we describe the structural, evolutionary and functional analysis of a protein with unknown function that served as a target in the recent CASP8 (Critical Assessment of Techniques for Protein Structure Prediction round 8). URL: http://prodata.swmed.edu/compas

Accurate statistical model of comparison between multiple sequence alignments

Comparison of multiple protein sequence alignments (MSA) reveals unexpected evolutionary relations between protein families and leads to exciting predictions of spatial structure and function. The power of MSA comparison critically depends on the quality of statistical model used to rank the similarities found in a database search, so that biologically relevant relationships are discriminated from spurious connections. Here, we develop an accurate statistical description of MSA comparison that does not originate from conventional models of single sequence comparison and captures essential features of protein families. As a final result, we compute E-values for the similarity between any two MSA using a mathematical function that depends on MSA lengths and sequence diversity. To develop these estimates of statistical significance, we first establish a procedure for generating realistic alignment decoys that reproduce natural patterns of sequence conservation dictated by protein secondary structure. Second, since similarity scores between these alignments do not follow the classic Gumbel extreme value distribution, we propose a novel distribution that yields statistically perfect agreement with the data. Third, we apply this random model to database searches and show that it surpasses conventional models in the accuracy of detecting remote protein similarities

Exploring the function and evolution of proteins using domain families

Proteins are frequently composed of multiple domains which fold independently. These are often evolutionarily distinct units which can be adapted and reused in other proteins. The classification of protein domains into evolutionary families facilitates the study of their evolution and function. In this thesis such classifications are used firstly to examine methods for identifying evolutionary relationships (homology) between protein domains. Secondly a specific approach for predicting their function is developed. Lastly they are used in studying the evolution of protein complexes. Tools for identifying evolutionary relationships between proteins are central to computational biology. They aid in classifying families of proteins, giving clues about the function of proteins and the study of molecular evolution. The first chapter of this thesis concerns the effectiveness of cutting edge methods in identifying evolutionary relationships between protein domains. The identification of evolutionary relationships between proteins can give clues as to their function. The second chapter of this thesis concerns the development of a method to identify proteins involved in the same biological process. This method is based on the concept of domain fusion whereby pairs of proteins from one organism with a concerted function are sometimes found fused into single proteins in a different organism. Using protein domain classifications it is possible to identify these relationships. Most proteins do not act in isolation but carry out their function by binding to other proteins in complexes; little is understood about the evolution of such complexes. In the third chapter of this thesis the evolution of complexes is examined in two representative model organisms using protein domain families. In this work, protein domain superfamilies allow distantly related parts of complexes to be identified in order to determine how homologous units are reused

STRUCTURE COMPARISON AND ALIGNMENT

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Probabilistic protein homology modeling

Searching sequence databases and building 3D models for proteins are important tasks for biologists. When the structure of a query protein is given, its function can be inferred. However, experimental methods for structure prediction are both expensive and time consuming. Fully automatic homology modeling refers to building a 3D model for a query sequence from an alignment to related homologous proteins with known structure (templates) by a computer. Current prediction servers can provide accurate models within a few hours to days. Our group has developed HHpred, which is one of the top performing structure prediction servers in the field. In general, homology based structure modeling consists of four steps: (1) finding homologous templates in a database, (2) selecting and (3) aligning templates to the query, (4) building a 3D model based on the alignment. In part one of this thesis, we will present improvements of step (2) and (4). Specifically, homology modeling has been shown to work best when multiple templates are selected instead of only a single one. Yet, current servers are using rather ad-hoc approaches to combine information from multiple templates. We provide a rigorous statistical framework for multi-template homology modeling. Given an alignment, we employ Modeller to calculate the most probable structure for a query. The 3D model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability density functions. We find that the query’s atomic distance restraints can be accurately described by two-component Gaussian mixtures. Moreover, we derive statistical weights to quantify the redundancy among related templates. This allows us to apply the standard rules of probability theory to combine restraints from several templates. Together with a heuristic template selection strategy, we have implemented this approach within HHpred and could significantly improve model quality. Furthermore, we took part in CASP, a community wide competition for structure prediction, where we were ranked first in template based modeling and, at the same time, were more than 450 times faster than all other top servers. Homology modeling heavily relies on detecting and correctly aligning templates to the query sequence (step (1) and (3) from above). But remote homologies are difficult to detect and hard to align on a pure sequence level. Hence, modern tools are based on profiles instead of sequences. A profile summarizes the evolutionary history of a given sequence and consists of position specific amino acid probabilities for each residue. In addition to the similarity score between profile columns, most methods use extra terms that compare 1D structural properties such as secondary structure or solvent accessibility. These can be predicted from local profile windows. In the second part of this thesis, we develop a new score that is independent of any predefined structural property. For this purpose, we learn a library of 32 profile patterns that are most conserved in alignments of remotely homologous, structurally aligned proteins. Each so called “context state” in the library consists of a 13-residue sequence profile. We integrate the new context score into our Hmm-Hmm alignment tool HHsearch and improve especially the sensitivity and precision of difficult pairwise alignments significantly. Taken together, we introduced probabilistic methods to improve all four main steps in homology based structure prediction

Protein structure prediction and structure-based protein function annotation

Nature tends to modify rather than invent function of protein molecules, and the log of the modifications is encrypted in the gene sequence. Analysis of these modification events in evolutionarily related genes is important for assigning function to hypothetical genes and their products surging in databases, and to improve our understanding of the bioverse. However, random mutations occurring during evolution chisel the sequence to an extent that both decrypting these codes and identifying evolutionary relatives from sequence alone becomes difficult. Thankfully, even after many changes at the sequence level, the protein three-dimensional structures are often conserved and hence protein structural similarity usually provide more clues on evolution of functionally related proteins. In this dissertation, I study the design of three bioinformatics modules that form a new hierarchical approach for structure prediction and function annotation of proteins based on sequence-to-structure-to-function paradigm. First, we design an online platform for structure prediction of protein molecules using multiple threading alignments and iterative structural assembly simulations (I-TASSER). I review the components of this module and have added features that provide function annotation to the protein sequences and help to combine experimental and biological data for improving the structure modeling accuracy. The online service of the system has been supporting more than 20,000 biologists from over 100 countries. Next, we design a new comparative approach (COFACTOR) to identify the location of ligand binding sites on these modeled protein structures and spot the functional residue constellations using an innovative global-to-local structural alignment procedure and functional sites in known protein structures. Based on both large-scale benchmarking and blind tests (CASP), the method demonstrates significant advantages over the state-of-the- art methods of the field in recognizing ligand-binding residues for both metal and non- metal ligands. The major advantage of the method is the optimal combination of the local and global protein structural alignments, which helps to recognize functionally conserved structural motifs among proteins that have taken different evolutionary paths. We further extend the COFACTOR global-to-local approach to annotate the gene- ontology and enzyme classifications of protein molecules. Here, we added two new components to COFACTOR. First, we developed a new global structural match algorithm that allows performing better structural search. Second, a sensitive technique was proposed for constructing local 3D-signature motifs of template proteins that lack known functional sites, which allows us to perform query-template local structural similarity comparisons with all template proteins. A scoring scheme that combines the confidence score of structure prediction with global-local similarity score is used for assigning a confidence score to each of the predicted function. Large scale benchmarking shows that the predicted functions have remarkably improved precision and recall rates and also higher prediction coverage than the state-of-art sequence based methods. To explore the applicability of the method for real-world cases, we applied the method to a subset of ORFs from Chlamydia trachomatis and the functional annotations provided new testable hypothesis for improving the understanding of this phylogenetically distinct bacterium