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Iddo Friedberg

Publications and source records attributed to Iddo Friedberg.

12 recordsLinked to original sources
PubMed2026

How not to be seen: predicting unseen enzyme functions using contrastive learning.

MOTIVATION: Predicting enzyme function from its sequence is still an unsolved problem in the life sciences. Moreover, with the explosion of annotated genome data, we are inundated with potential enzymatic sequences that have not yet been biochemically characterized. While it is not possible to assign a not-yet-existing label to such a sequence, there is high value in placing the sequence as accurately as possible in known function space. Doing so can help provide more accurate falsifiable hypotheses for experimentalists wishing to characterize enzymes from specific functional families. RESULTS: Here we present a contrastive learning algorithm for predicting enzyme function from sequence. Our method, EnzPlacer, predicts the third, second, and first EC numbers for a protein whose fourth EC number is not in the training corpus. This novel prediction mechanism accurately places a protein sequence within a narrowed-down functional context, even if the precise function remains unknown. AVAILABILITY AND IMPLEMENTATION: EnzPlacer and data is available at https://github.com/drxiangma/EnzPlacer under a GPL3 license.

Enzymes↗
PubMed2026

How Not to be Seen: Predicting Unseen Enzyme Functions using Contrastive Learning.

MOTIVATION: Predicting enzyme function from its sequence is still an unsolved problem in the life sciences. Moreover, with the explosion of annotated genome data, we are inundated with potential enzymatic sequences that have not yet been biochemically characterized. While it is not possible to assign a not-yet-existing label to such a sequence, there is high value in placing the sequence as accurately as possible in known function space. Doing so can help provide more accurate falsifiable hypotheses for experimentalists wishing to characterize enzymes from specific functional families. RESULTS: Here we present a contrastive learning algorithm for predicting enzyme function from sequence. Our method, EnzPlacer, predicts the third, second, and first EC numbers for a protein whose fourth EC number is not in the training corpus. This novel prediction mechanism accurately places a protein sequence within a narrowed-down functional context, even if the precise function remains unknown. AVAILABILITY: EnzPlacer is available from https://github.com/drxiangma/EnzPlacer under a GPL3 license.

Contrastive learning↗
PubMed2006

JAFA: a protein function annotation meta-server.

With the high number of sequences and structures streaming in from genomic projects, there is a need for more powerful and sophisticated annotation tools. Most problematic of the annotation efforts is predicting gene and protein function. Over the past few years there has been considerable progress in automated protein function prediction, using a diverse set of methods. Nevertheless, no single method reports all the information possible, and molecular biologists resort to 'shopping around' using different methods: a cumbersome and time-consuming practice. Here we present the Joined Assembly of Function Annotations, or JAFA server. JAFA queries several function prediction servers with a protein sequence and assembles the returned predictions in a legible, non-redundant format. In this manner, JAFA combines the predictions of several servers to provide a comprehensive view of what are the predicted functions of the proteins. JAFA also offers its own output, and the individual programs' predictions for further processing. JAFA is available for use from http://jafa.burnham.org.

Internet↗
PubMed2006

Automated protein function prediction--the genomic challenge.

Overwhelmed with genomic data, biologists are facing the first big post-genomic question--what do all genes do? First, not only is the volume of pure sequence and structure data growing, but its diversity is growing as well, leading to a disproportionate growth in the number of uncharacterized gene products. Consequently, established methods of gene and protein annotation, such as homology-based transfer, are annotating less data and in many cases are amplifying existing erroneous annotation. Second, there is a need for a functional annotation which is standardized and machine readable so that function prediction programs could be incorporated into larger workflows. This is problematic due to the subjective and contextual definition of protein function. Third, there is a need to assess the quality of function predictors. Again, the subjectivity of the term 'function' and the various aspects of biological function make this a challenging effort. This article briefly outlines the history of automated protein function prediction and surveys the latest innovations in all three topics.

Algorithms↗
PubMed2005

Fragnostic: walking through protein structure space.

The Fragnostic (http://ffas.burnham.org/Fragnostic) web tool implements a novel and useful view of protein structure space. We mined a non-redundant subset of the PDB for common fragments shared between proteins inhabiting different SCOP folds. Subsequently, we formulated an inter-fold similarity measure based on fragment sharing. Fold space is described as a graph whose nodes are folds between which the edges are drawn depending on the extent of fragment sharing. In this fashion, Fragnostic helps discover meaningful relationships between proteins belonging to different folds, based on sharing similar fragments in the proteins comprising those folds. Distant fold similarity information is supplemented by annotations taken from Gene Ontology, SCOP and CATH. Overall, Fragnostic is a tool which helps discover structural and functional relationships between proteins which are distantly related or seemingly unrelated.

Computer Graphics↗
PubMed2005

Connecting the protein structure universe by using sparse recurring fragments.

The quest to order and classify protein structures has lead to various classification schemes, focusing mostly on hierarchical relationships between structural domains. At the coarsest classification level, such schemes typically identify hundreds of types of fundamental units called folds. As a result, we picture protein structure space as a collection of isolated fold islands. It is obvious, however, that many protein folds share structural and functional commonalities. Locating those commonalities is important for our understanding of protein structure, function, and evolution. Here, we present an alternative view of the protein fold space, based on an interfold similarity measure that is related to the frequency of fragments shared between folds. In this view, protein structures form a complicated, crossconnected network with very interesting topology. We show that interfold similarity based on sequence/structure fragments correlates well with similarities of functions between protein populations in different folds.

Peptide Library↗
PubMed2004

Protein tyrosine phosphatases in the human genome.

Tyrosine phosphorylation is catalyzed by protein tyrosine kinases, which are represented by 90 genes in the human genome. Here, we present the set of 107 genes in the human genome that encode members of the four protein tyrosine phosphatase (PTP) families. The four families of PTPases, their substrates, structure, function, regulation, and the role of these enzymes in human disease will be discussed.

Amino Acid Sequence↗
PubMed2004

The interplay of fold recognition and experimental structure determination in structural genomics.

Achieving the goals of structural genomics initiatives depends on the outcomes of two groups of factors: the number and distribution of experimentally determined protein structures, and our ability to assign novel proteins to known structures (fold recognition) and use them to build models (modeling). The quality of the tools used for fold recognition defines the scope of experimental effort - the more distant the templates that can be recognized, the smaller the number of proteins that have to be solved. Recent improvements in fold recognition may have suggested that the goals of structural genomics initiatives are getting closer. However, problems that surfaced during the first few years of active work have put many of the early estimates in doubt and new ones are still slow in coming.

Animals↗
PubMed2002

PeCoP: automatic determination of persistently conserved positions in protein families.

UNLABELLED: PeCoP is a WWW-based service which accepts a protein sequence, and reports positions that are conserved in close and distant sequence family members. The collation of family members is performed using iterative PSI-BLAST runs. Examining positional conservation in close and distant family members enables a better selection of positions that may play a role in determining the protein's structure and function. AVAILABILITY: http://bioinformatics.org/pecop CONTACT: idoerg@burnham.org SUPPLEMENTARY INFORMATION: http://bioinformatics.org/pecop/about_pecop

Amino Acid Sequence↗
PubMed2002

Persistently conserved positions in structurally similar, sequence dissimilar proteins: roles in preserving protein fold and function.

Many protein pairs that share the same fold do not have any detectable sequence similarity, providing a valuable source of information for studying sequence-structure relationship. In this study, we use a stringent data set of structurally similar, sequence-dissimilar protein pairs to characterize residues that may play a role in the determination of protein structure and/or function. For each protein in the database, we identify amino-acid positions that show residue conservation within both close and distant family members. These positions are termed "persistently conserved". We then proceed to determine the "mutually" persistently conserved (MPC) positions: those structurally aligned positions in a protein pair that are persistently conserved in both pair mates. Because of their intra- and interfamily conservation, these positions are good candidates for determining protein fold and function. We find that 45% of the persistently conserved positions are mutually conserved. A significant fraction of them are located in critical positions for secondary structure determination, they are mostly buried, and many of them form spatial clusters within their protein structures. A substitution matrix based on the subset of MPC positions shows two distinct characteristics: (i) it is different from other available matrices, even those that are derived from structural alignments; (ii) its relative entropy is high, emphasizing the special residue restrictions imposed on these positions. Such a substitution matrix should be valuable for protein design experiments.

Amino Acid Motifs↗