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1d.qmd
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---
title: "1D Features"
date: "August 21, 2023"
date-modified: "`r Sys.Date()`"
format:
html:
page-layout: full
toc: true
toc-location: right
toc-depth: 3
number-sections: true
number-depth: 1
link-external-icon: true
link-external-newwindow: true
bibliography: references.bib
editor:
markdown:
wrap: 80
---
```{r echo=FALSE, output=FALSE}
library(webexercises)
```
By definition 1D features are protein features that can be decoded directly from
the protein primary structure and represented as values (categories, %, ...)
associated to individual residues in the sequence. For instance, we can assign a
secondary structure state (symbol or probability) to each residue. Many
structure prediction methods implement or call third parties methods to predict
secondary structure and other 1D features, as important additional information
during modeling process.
You can find links to several 1D features prediction tools in the [Modeling
Resources](links.html) section.
# Protein secondary structure prediction
## Multiple state of secondary structures
Secondary structures are assigned to structures using **DSSP** (*Define
Secondary Structure of Proteins*) algorithm, originally written in 1983 and
updated several times throughout the years, being the last version from 2021
(available on [GitHub](https://github.com/PDB-REDO/dssp)). This algorithm
classifies each residue considering its geometry and H-bonds prediction by
comparison with pre-existing patterns in DSSP database. Remarkably, [DSSP does
not predict]{.underline} secondary structures, it just extracts this information
from the 3D coordinates.
Most protein secondary structure prediction (**PSSP**) methods use a three-state
secondary structure, in which the secondary structure elements consist of helix
(H), sheet (E), and coil (C). Helix and sheet are the two main conformations
suggested in early times of structural biology by Linus Pauling (see [Intro
Lesson](intro.html#ss)), whereas Coil (C) denotes an amino acid that does not
fit both H and E. This representation is very important and still used by many
structural biologist, however it imposes several limitations that cannot be
overlooked. This is because three secondary structure states are only a
coarse-grained representation of the backbone structure with helical and sheet
residues that very often can deviate from the standard helix and sheet
conformations.
Already in the 80s, a eight-state secondary structure was proposed (see
@ismi2022), consisting in α-helix (H), 3~10~-helix (G), parallel/anti-parallel
β-sheet (E), isolated β-bridge (B), bend (S), turn (T), π-helix (I) and coli
(C). In fact, DSSP, defines the eight states in experimentally obtained
structures and it also contains transformations that rule the mapping of
eight-state secondary structures to the three-states.
More recently, in 2020, four-state and five-state PSSP were proposed to simplify
predictions and increase the true-positives. The reason for proposing four-state
and five-state PSSP was the imbalanced samples of each class: isolated β-bridge
(B) and bend (S) have a small number of samples and low true-positive rates. In
five-state PSSP, B and S are considered as C, whereas in four-state PSSP, B, S,
and G are considered as C. Moreover, 75% of π-helix (I) was located at the
beginning or the end of an a-helix structure (H), so it was categorized as H.
The full potential of this new categories is still to be analyzed in detail
## Evolution of Prediction methods
Protein secondary structure prediction (PSSP) from protein sequences is based in
the hypothesis that Segments of consecutive residues have preferences for
certain secondary structure states. Similar to other methods in bioinformatics,
including protein modeling, approaches to SS prediction evolved during the last
50 years (see Table 1).
First generation methods rely on statistics approaches and prediction depends on
assigning a set of prediction values to a residue and then applying a simple
algorithm to those numbers. I.e. apply a probability score based on single amino
acid propensity. In the 1990's, new algorithms included the information of the
flanking residues (3-50 nearby amino acids) in the so-called Nearest Neighbor
(N-N) Methods. These methods increased the accuracy in many cases but still had
strong limitations, as they only considered three possible states (helix, strand
or turn). Moreover, as you know from the secondary structure practice, β-strands
predictions are more difficult and did not improve much thanks to N-N methods.
Additionally, predicted helices and strands were usually too short.
By the end of 1990 decade, new methods boosted the accuracy to values near to
80%. These methods included two innovations, one conceptual and one
methodological. The conceptual innovation was the inclusion of evolutionary
information in the predictions, by considering the information of multiple
sequence alignments or profiles. If a residue or a type of residue is
evolutionary conserved, it is likely that it is important to define SS
stretches. The second innovation was the use of neural networks (see
[below](#sec-NN)) in which multiple layers of sequence-to-structure predictions
were compared with a independently trained networks (see PHD paper by Burkhard
Rost [here](https://www.rostlab.org/papers/1996_phd/paper.html)).
Since the 2000s, most commonly used methods are meta-servers that compare
several algorithms, mostly based o neural-networks, like
[JPred](http://www.compbio.dundee.ac.uk/jpred/) or
[SYMPRED](https://www.ibi.vu.nl/programs/sympredwww/), among others.
In the recent years, deep neural networks trained with large datasets have
become the primary method for protein secondary structure prediction (and almost
any other prediction in StrBio). In the Alphafold era (see [last](ai.html)
lesson), methods adapted from image processing or natural language processing
(NLP) are also used (for instance in NetSurfP-3.0, see @høie2022), allowing
protein secondary structure predictions to focus on specific objectives, such as
enhancing the quality of evolutionary information for protein modeling
[@ismi2022].
+------------------+-------------------------------------+-------------------+
| **Generation** | **Method** | **Accuracy** |
+------------------+-------------------------------------+-------------------+
| 1st: Statistics | Chow & Fassman (1974-) | 57% |
+------------------+-------------------------------------+-------------------+
| | GOR (1978-) | |
+------------------+-------------------------------------+-------------------+
| 2nd: Nearest | PREDATOR (1996) | 75% |
| Neighbor (N-N) | | |
| methods | | |
+------------------+-------------------------------------+-------------------+
| | NNSSP (1995) | 72% |
+------------------+-------------------------------------+-------------------+
| 3rd: N-N neural | APSSP | Up to 86% |
| network & | | |
| evolutionary | | |
| info | | |
+------------------+-------------------------------------+-------------------+
| | PsiPRED (1999-) | 75.7% (1999) |
| | | |
| | | 84% (2019) |
+------------------+-------------------------------------+-------------------+
| | PHD (1997) | |
+------------------+-------------------------------------+-------------------+
| 4th: Multiple | Extra layers of info, such as | \<80% |
| layers of info | conserved domains, frequent | |
| | patterns, contact maps or predicted | |
| | residue solvent accessibility | |
| | (2000s) | |
+------------------+-------------------------------------+-------------------+
| 5th generation | Sophisticated deep learning | \>80% |
| | architectures and NLP (2010s, | |
| | 2020s). | |
| | | |
| | RaptorX-Property, (2018), SPIDER3 | |
| | (2020) and NetSurfP-3.0 (2022), | |
| | among others. | |
+------------------+-------------------------------------+-------------------+
| **META-Servers** | Jpred4 | |
+------------------+-------------------------------------+-------------------+
| | GeneSilico (Discontinued) | |
+------------------+-------------------------------------+-------------------+
| | SYMPRED | |
+------------------+-------------------------------------+-------------------+
: Evolution of secondary structure prediction methods (modified from @ismi2022).
# Structural disorder and solvent accessibility
The expression *disorder* denote protein stretches that cannot be assigned to
any SS. They are usually dynamic/flexible, thus with high B-factor or even
missing in crystal structures. These fragments show a low complexity and they
are usually rich in polar residues, whereas aromatic residues are rarely found
in disordered regions. These motifs are usually at the ends of proteins or
domain boundaries (as linkers). Additionally, they are frequently related to
specific functionalities, such in the case of proteolytic targets or
protein-protein interactions (PPI). More rarely, large disordered domains can be
conserved in protein families and associated with relevant functions, as in the
case of some transcription factors, transcription regulators, kinases...
There are many methods and servers to predict disordered regions. You can see a
list in the Wikipedia
[here](https://en.wikipedia.org/wiki/List_of_disorder_prediction_software) or in
the review by @atkins2015. The best-known server is
[DisProt](https://www.disprot.org/), which uses a large curated database of
intrinsically disordered proteins and regions from the literature, which has
been recently improved to version 9 in 2022, as described in @quaglia2022.
Interestingly, a low plDDT (see [below](ai.html#sec-AF2)) score in Alphafold2
models has been also suggested as a good indicator of protein disorder
[@wilson2022].
.](pics/imageSwap.gif){#fig-disprot
.figure}
*Hydrophobic collapse* is usually referred to as a key step in protein folding.
Hydrophobic residues tend to be buried inside the protein, whereas hydrophilic,
polar amino acids are exposed to the aqueous solvent.
.](pics/collapse.png "Hydrophobic collapse"){#fig-collapse
.figure}
Solvent accessibility correlates with residue hydrofobicity (accessibility
methods usually better performance). Therefore, estimation of how likely each
residue is exposed to the solvent or buried inside the protein is useful to
obtain and analyze protein models. Moreover, this information is useful to
predict PPIs as well as ligand binding or functional sites. Most methods only
classify each residue into two groups: Buried, for those with relative
accessibility probability \<16% and Exposed, for accessibility residues \>16%.
Most common recent methods, like [ProtSA](http://webapps.bifi.es/ProtSA/) or
[PROFacc](www.rostab.org), combine evolutionary information with neural networks
to predict accessibility.
# Trans-membrane motifs and membrane topology
Identification of transmembrane motifs is also a key step in protein modeling.
About 25-30% of human proteins contain transmembrane elements, most of them in
alpha helices.
{#fig-TM .figure}
The PDBTM ([Protein Data Bank of Transmembrane
Proteins](http://pdbtm.enzim.hu/)) is a comprehensive and up-to-date
transmembrane protein selection. As of September 2022, it contains more than
7600 transmembrane proteins, 92.6% of them with alpha helices TM elements. This
number of TM proteins is relatively low, as compared with more than 160k
structures in PDB, as TM proteins are usually harder to purify and
crystalization conditions are often elusive. Thus, although difficult, accurate
predictions of TM motifs and overall protein topology can be essential to define
protein architecture and identify domains that could be structurally or
functionally studied independently.
{#fig-topo .figure}
Current state-of-the-art TM prediction protocols show an accuracy of 90% for
definition of TM elements, but only a 80% regarding the protein topology.
However, some authors claim that in some types of proteins, the accuracy is not
over 70%, due to the small datasets of TM proteins. Most recent methods, based
in deep-learning seem to have increased the accuracy to values near 90% for
several groups of proteins [@hallgren].
# Subcellular localization tags and post-translational modification sites
Many cellular functions are compartmentalized in the nucleus, mitochondria,
endoplasmatic reticulum (ER), or other organules. Therefore, many proteins
should be located in those compartments. That is achieved by the presence of
some labels, in form of short peptidic sequences that regulate traffic and
compartmentalization of proteins within the cells. Typically, N-terminal signals
direct proteins to the mitochondrial matrix, ER, or peroxisomes, whereas nucleus
traffic is regulated by nuclear localization signals (NLS) and nuclear export
signals (NES).
Similarly, post-translational modifications very often occur in conserved motifs
that contain target residues for phosphorylation or ubiquitination, among other
modifications.
These short motifs are difficult to predict, as datasets of validated signals
are small. The use of consensus sequences allowed predictions, although in many
cases with a high level of uncertainty.